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Strommen et al. (2026) Corrigendum
This paper corrects a previous error regarding the comparison of precipitation variability between coupled climate models and models run with prescribed sea surface temperatures (AMIP models).
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Raghuvanshi et al. (2026) Network Divergence Reveals Predictable Pathways of Extreme Rainfall in Central India
The study introduces a network-based framework using nonlinear synchronization to predict extreme rainfall in Central India, successfully forecasting over 60% of events exceeding the 95th percentile.
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Sandford et al. (2026) Addressing the World War 2 Warm Anomaly in HadSST.4.2.0.0
This study presents an update to the HadSST dataset (version 4.2.0.0) to eliminate a residual warm bias during the Second World War (WW2) by refining Engine Room Intake (ERI) bias corrections and addressing truncation bias in Japanese observations.
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Vidushi et al. (2026) Flash Drought Dynamics and Vegetation Responses to Soil Moisture and Vapor Pressure Deficit in India
This study investigates the impacts of soil moisture (SM) and vapor pressure deficit (VPD) on vegetation productivity during flash droughts (FDs) across India, highlighting biome-specific sensitivities and recovery dynamics.
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Morakinyo et al. (2026) Multi‐Country‐Multi‐City Characterisation of Heat Stress and Exposure in Africa
This study provides the first continent-wide assessment of summer heat stress in Africa from 1974 to 2023, revealing a significant increase in the frequency, duration, and intensity of extreme heat events.
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Wang et al. (2026) Multidimensional evaluation of the gridded precipitation datasets over the source region of the Yellow River
The study evaluates five existing precipitation datasets in the Source Region of the Yellow River (SRYR) to develop a new, high-accuracy gridded dataset (CCLI) using the delta correction method. The resulting CCLI dataset outperformed the others in accuracy and uncertainty, revealing a long-term precipitation increase of 2.03 mm/yr in the region since the 1950s.
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Gupta et al. (2026) Dominant Role of Soil Moisture Regulating Vegetation Water Content During Flash Drought Evolution
This study investigates the impact of concurrent and antecedent rootzone soil moisture (SM) and vapor pressure deficit (VPD) on vegetation optical depth (VOD) during flash droughts, concluding that SM is the primary driver of global ecosystem responses.
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Chen et al. (2026) Resilience assessment of an integrated green-grey-blue-control system for urban flood risk mitigation under changing climate and urbanization
The study proposes an Integrated Green-Grey-Blue-Control (IGGBC) system that combines structural and non-structural measures to enhance urban flood resilience, finding that this integrated approach significantly outperforms single-type mitigation strategies in reducing inundation and economic loss.
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Biagioli et al. (2026) Spatial Patterns of Shallow Clouds: Challenging the Concept of Defined Regimes
The study utilizes the $L$-function to analyze the spatial organization of tropical shallow clouds in the western Atlantic, concluding that cloud organization exists as a continuum rather than as discrete archetypal regimes.
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Szwed et al. (2026) Polish Agriculture in the Face of Climate Change: Better or Worse?
This study evaluates the impact of climate change on Poland's agricultural growing season from 1961 to 2020, finding that while rising temperatures have extended the growing season, they have simultaneously intensified the risk of summer droughts.
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Wang et al. (2026) Planetary climate interactions of the Qinghai-Tibetan Plateau
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Pierini et al. (2026) Framework for global emulation of extreme precipitation along global warming trajectories
The study introduces a climate emulator within the MESMER-X framework designed to generate spatially resolved time series of annual maximum daily precipitation conditioned on global mean temperature trajectories.
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Zhang et al. (2026) Process‐Oriented Calibration of a Turbulence Scheme in the DOE's Global Storm‐Resolving Model Using Machine Learning
A machine learning-based calibration framework was developed for the SHOC turbulence scheme in the SCREAM model, significantly improving the representation of boundary-layer turbulence and shallow cumulus clouds.
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Hang et al. (2026) CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
CycloneMAE is a multi-task masked autoencoder model that leverages multi-modal data to provide scalable, probabilistic, and interpretable tropical cyclone forecasts. It outperforms leading numerical weather prediction (NWP) systems in predicting pressure, wind, and track across five global ocean basins.
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Duan et al. (2026) Land Surface Temperature Shows Negligible Difference Between Inside and Outside Photovoltaic Power Plants in China
This study investigates the effects of ground-mounted photovoltaic (PV) power plants on land surface temperature (LST) across China, finding that PV plants generally induce daytime warming (0.10 °C) and nighttime cooling (−0.09 °C), with effects varying by vegetation type and season.
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Yang et al. (2026) Uniformity in Heavy Precipitation Microphysics During the Northward Advancement of Summer Monsoon in China Unveiled by Objective Weather Typing
This study isolates canonical East Asian summer monsoon precipitation using objective synoptic classification of satellite observations, revealing its microphysics are highly uniform and dominated by warm-rain accretion across China, in contrast to non-monsoon systems which favor ice-phase processes.
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Wheen (2026) A First Principles Approach to the 100,000-year Problem
This study evaluates competing astronomical and geochemical theories of glacial-interglacial cycles, concluding that a linear astronomical feedforward model can largely reproduce the 800,000-year ice volume record.
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Chen et al. (2026) Structural Gating and Effect-aligned Lag-resolved Temporal Causal Discovery Framework with Application to Heat-Pollution Extremes
The study introduces SGED-TCD, a general framework for lag-resolved causal discovery in multivariate time series, and applies it to identify the drivers of compound heatwave-air-pollution extremes in China.
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Chen et al. (2026) Reduced Spring Extratropical Cyclone Activity Over the East Asian Subtropical Region has Suppressed Regional Precipitation From 1979 to 2023
This study analyzes extratropical cyclone (EC) characteristics and their linkage to precipitation in the East Asian subtropical region during spring (1979–2023), revealing significant decreasing trends in both EC genesis and precipitation, primarily driven by non-uniform near-surface warming that suppresses ECs, subsequently weakening dynamic ascent and moisture transport.
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Li et al. (2026) Subseasonal Forecasting of Snow Cover and Cold Compound Extremes: Insights From MPAS‐A Over Midlatitude East Asia
This study evaluates the subseasonal forecast skill of snow cover and cold compound extremes in midlatitude East Asia using MPAS-A, finding detectable skill up to three pentads, but highlighting that biases from underestimated snowfall and the choice of snow cover fraction scheme significantly impact forecast accuracy.
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Yang et al. (2026) Leaf thermal infrared imaging and lightweight deep learning enable early detection of water stress in watermelon for precision irrigation
This study proposes a thermal-imaging-based deep learning approach to classify watermelon water-stress status for precision irrigation. It systematically evaluates nine deep learning models, identifying EfficientNet-B0 as the most suitable for field deployment due to its optimal balance of high accuracy (0.99) and computational efficiency (0.39 GFLOPs, 8.81 ms inference latency).
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Bai et al. (2026) The quantity of soil moisture replenishment rather than soil water content controls larch growth in semi-arid mountainous areas, Northwest China
This study investigated the control of larch growth by soil moisture replenishment (SMR) versus soil water content in semi-arid mountainous areas of Northwest China, finding that SMR is a primary determinant, especially for stem growth, and significantly influences water use efficiency. The findings suggest SMR should be a key indicator for ecological restoration in these water-limited regions.
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Mamgain et al. (2026) A satellite-based forest fire weather index for characterizing fire danger variability in the Himalaya
This study develops a satellite-based Forest Fire Weather Index (FFWI) for the data-scarce Himalayan region, integrating five satellite-derived indicators to provide spatially explicit fire danger assessments, demonstrating strong spatiotemporal variability and a robust link to large-scale climate phenomena.
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Beccaro et al. (2026) Assessing English peatland dynamics using MT-InSAR
This study applies the Enhanced Persistent Scatterers (E-PS) multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method to three English peatland sites, revealing widespread ground subsidence (up to -10 mm/year) driven by vegetation degradation, land management practices, and fluctuating water levels, indicating that peat degradation currently outpaces formation.
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Ayari et al. (2026) Comparing 1-km Sentinel-1 surface soil moisture with coarser-resolution satellite data for agricultural drought monitoring in Mediterranean regions
This study evaluates the potential of a 1-kilometer surface soil moisture (SSM) product (HRSM) derived from Sentinel-1 and Sentinel-2 data for agricultural drought monitoring in Mediterranean regions, comparing its performance with coarser-resolution satellite SSM products (SMAP, ESA CCI) and root zone soil moisture (RZSM). The HRSM product shows good coherence with coarser products, uniquely identifying drought in agricultural areas, but its lower revisit frequency can miss short-duration rainfall events compared to daily or sub-daily products.
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Jacobs et al. (2026) GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar
This paper introduces GreenScatter, a physics-based framework for retrieving soil moisture using nadir-looking wideband UAV radars, specifically addressing the challenge of vegetation-soil electromagnetic coupling. The framework demonstrates consistent soil moisture estimation through vegetation with an average volumetric water content error of 4.49% in field experiments.
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Makarieva et al. (2026) On the Methodology for Assessing Vegetation Impacts on the Atmospheric Branch of the Hydrological Cycle
This paper critically evaluates existing methodologies for assessing the impact of large-scale vegetation restoration on the hydrological cycle in China, arguing that neglecting vegetation-induced atmospheric circulation changes biases results and proposing that initial streamflow reductions may be a transient phase that could reverse over time.
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Ma et al. (2026) A spatiotemporally differentiated hybrid hydrological modeling strategy with dynamically adaptive runoff generation modes
This study develops a spatiotemporally differentiated hybrid hydrological model (STHM) that dynamically adapts runoff generation modes using machine learning to improve flood forecasting in small-to-medium basins. The STHM demonstrates superior and more stable simulation performance compared to conventional models, effectively capturing event-dependent runoff processes.
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Rafter et al. (2026) Trends in Annual Maximum Sub‐Daily to Daily Precipitation Over Australia
This study evaluates trends in annual and seasonal maxima of sub-daily precipitation accumulations (1 to 24 hours) across Australia, revealing increasing hourly rainfall trends, particularly in austral summer, but decreasing trends at longer sub-daily durations and in austral autumn.
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Iwakiri et al. (2026) ENSO Recharge Oscillator Theory Integrating the Southward Wind Shift
This study derives a new form of the Recharge Oscillator (RO) theory for ENSO that explicitly incorporates the seasonal migration of equatorial zonal wind anomalies. The new theory demonstrates how Bjerknes feedback depends on the central latitude of these wind anomalies and shows that a stochastic RO simulation can reproduce key ENSO characteristics like seasonal synchronization and combination tones.
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Ye et al. (2026) An IoT-based predictive irrigation scheduling framework for precision soil moisture control in greenhouses
This study developed an IoT-based predictive irrigation framework that optimizes soil moisture control by establishing a proportional relationship (Δtpred = ½Δteval) between evaluation and prediction intervals, demonstrating stable moisture regulation and reduced irrigation frequency in greenhouse experiments.
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Burić (2026) Are Changes in Seasonal and Annual Precipitation in the Balkan Peninsula Driven by Increases in Anthropogenic Greenhouse Gases or by Teleconnection Variability?
This study analyzes precipitation trends and their drivers in the Balkan Peninsula from 1950 to 2024 using ERA5-Land and GPCC datasets, revealing widespread seasonal and annual precipitation decreases primarily influenced by atmospheric oscillations, with limited impact from anthropogenic greenhouse gases or oceanic oscillations.
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Cai et al. (2026) Characterizing Temperature and Precipitation Tails via Expected Shortfall Regression
This study applies Expected Shortfall (ES) regression to analyze temperature and precipitation trends in the continental U.S. from 1950-2015, revealing distinct spatial and temporal changes in the tails of distributions, including significant decadal temperature increases in the southern and central U.S. and ENSO's influence on extreme events.
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Giménez et al. (2026) Subsurface conditions and hydrologic accumulation drive stream connectivity and flow intermittency in urban river networks
This study investigated the controls on stream network connectivity and flow intermittency in the Little Calumet River Watershed, USA, revealing that subsurface conditions and hydrologic accumulation are primary drivers, challenging the prevailing view that impervious cover dictates these dynamics.
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Hana et al. (2026) Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting
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Fons et al. (2026) Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic
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Song et al. (2026) Observationally constrained global warming hysteresis under CO2 removal
This study develops an observationally constrained data-driven emulator to quantify global warming hysteresis under carbon dioxide (CO2) removal. It projects that global warming during the CO2 removal phase will be 0.7 °C warmer than during the CO2 increasing phase at the same CO2 concentration, leading to a temporary overshoot of the 1.5 °C target even under stringent mitigation.
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Liu et al. (2026) Urban Land Surface Effects on Summertime Clouds and Moist Convection in Houston Under Different Synoptic Conditions
This study investigates the impact of the Houston metropolitan area on summertime cloud cover and convective cell characteristics using satellite observations, radar data, and high-resolution modeling. It reveals that urban land cover consistently enhances cloud fraction and convective activity, with these impacts being highly dependent on large-scale meteorological forcing.
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Dorrington et al. (2026) Dynamically‐Informed Extreme Event Attribution Using Circulation Imprints
This paper introduces a novel extreme event attribution approach that isolates dynamical contributions from other factors to changing extreme event probability, demonstrating its application to recent wildfires, floods, and storms, and finding varying climate change impacts across these event types.
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Lu et al. (2026) Nonlinear Convection–SST Sensitivity as a Bridge for the Asymmetric Low‐Level Wind Response to ENSO
This study investigates the cause of the observed asymmetry in atmospheric responses to El Niño and La Niña, revealing that it stems from the nonlinear sensitivity of atmospheric convection to sea surface temperature in the tropical Pacific.
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Talvinen et al. (2026) Beyond cloud cover: Low- and high-altitude clouds have distinct impacts on tree sap flow and transpiration
This study investigates how different cloud types affect tree transpiration in European boreal and temperate forests using long-term sap flow and surface-based cloud observations. It reveals that low-altitude clouds significantly suppress transpiration, while high-altitude clouds have a negligible effect, with declining low-altitude cloud cover potentially increasing annual transpiration by 0.6–1.2 mm in boreal forests.
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Liu et al. (2026) Intraseasonal Northwest–Southeast Oscillation of the Tropical Easterly Jet Core and Its Modulation by Atmospheric Internal Dynamics and Boreal Summer Intraseasonal Oscillation
This study identifies and explains a distinct northwest-southeast intraseasonal oscillation of the Tropical Easterly Jet (TEJ) core during boreal summer, revealing its primary governance by upper-tropospheric dynamic processes and modulation by the northward propagation of large-scale convective anomalies associated with the Boreal Summer Intraseasonal Oscillation (BSISO).
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Vacca et al. (2026) Subseasonal variability of the winter North Atlantic jet stream has decreased due to climate change
This study reveals a significant decrease in the subseasonal variability of the winter North Atlantic eddy-driven jet's latitude and tilt over the past 75 years due to climate change, with models projecting continued reduction throughout the 21st century.
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Shi et al. (2026) Log-Laplace Nuggets for Fully Bayesian Fitting of Spatial Extremes Models to Threshold Exceedances
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Madadikhaljan et al. (2026) Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
[A VERY CONCISE 1-2 sentence summary of the paper's core objective and main finding.]
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Wisinski et al. (2026) What's in the latent space? Exploring coupled tropical Pacific variability within a Multi-branch $β$-Variational Autoencoder
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Bhattacharjee et al. (2026) Eliciting core spatial association from spatial time series: a random matrix approach
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Montazar et al. (2026) Field-scale evaluation of OpenET for quantifying consumptive water savings under deficit irrigation in alfalfa
This study evaluated OpenET satellite-derived actual crop evapotranspiration (ETc act) for quantifying consumptive water savings under summer deficit irrigation in alfalfa fields. It found that OpenET, particularly its ensemble product, reliably quantifies significant water savings (150–200 mm per deficit period, 40–50% reduction) in arid agricultural systems, validated by eddy covariance and soil moisture data.
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Siirila-Woodburn et al. (2026) Warming and snow loss increase reliance on old groundwater in a Colorado River headwater
Integrated hydrologic modeling in the Upper Colorado River headwaters reveals that atmospheric warming and snow loss increase reliance on older groundwater for streamflow, leading to disproportionate declines in high-elevation groundwater storage and an overall aging of streamflow contributions.
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Guan et al. (2026) A framework of coupling split-window and machine learning (SW-ML) for land surface temperature retrieval from MODIS thermal infrared data
This study proposes an SW-ML framework integrating physics-based split-window (SW) and machine learning (XGBoost) to accurately retrieve land surface temperature (LST) from MODIS thermal infrared data, demonstrating improved accuracy and interpretability, especially under challenging environmental conditions.
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Tiwari et al. (2026) Transition in Köppen Climate Zones and Its Impacts on Hydroclimatic Extremes Across India
This study investigates how spatial shifts in Köppen–Geiger climate zones across India between 1961–1990 and 1991–2020 influence long-term drought characteristics, revealing an expansion of arid zones and contraction of temperate zones, leading to more frequent and intense droughts driven by rising temperatures and increased evapotranspiration.
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Zhu et al. (2026) DeepProfile: An inverse fusion framework for root zone soil moisture profile estimation
This study introduces DeepProfile, an inverse fusion framework that integrates multiple heterogeneous root zone soil moisture (RZSM) products to estimate continuous soil moisture profiles down to 1 meter, demonstrating strong global agreement with in-situ measurements.
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Bartfeld et al. (2026) Intrinsic predictability of heavy precipitation influenced by atmospheric rivers in the Western Iberian Peninsula
This study investigates the dynamics and intrinsic predictability of Heavy Precipitation Events (HPE) in Portugal, emphasizing the role of Atmospheric Rivers (AR). It reveals that AR-linked HPE are more intense and predictable, primarily driven by strong low-level winds, with high-predictability events associated with well-defined, deep extra-tropical cyclones.
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Ling et al. (2026) Advancing the understanding of drought propagation in glacierized catchments by accounting for meltwater and catchment control in a newly introduced bi-stage framework
This study developed a novel bi-stage drought propagation analysis framework (BDPF) to investigate the complex process of meteorological to hydrological drought in glacierized catchments, revealing that meltwater mitigates drought severity while catchment controls tend to amplify it.
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Cui et al. (2026) A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting
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Alkhawlani et al. (2026) Quantified groundwater–surface water dynamics across Tigris–Euphrates basin through integrated modelling
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Cai et al. (2026) Compound Hot‐Dry Extremes Amplify Disproportionate Climate Risks for Low‐Income Nations
This study provides a global and cross-national assessment of future risks from compound hot-dry extremes, revealing that under current policies leading to approximately 2.7°C warming by 2100, 28.5% ± 9.3% of the global population (approximately 2.6 ± 0.9 billion people) may face heightened exposure, with low-income and tropical island nations disproportionately affected.
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Matsuo et al. (2026) Probabilistic Assessments on Future Changes in Typhoon Characteristics Based on Fixed-SST Ensemble Experiments by Slab-Ocean Coupled MRI-AGCM
This study probabilistically assessed the variability and future changes in typhoon intensity and frequency using new large ensemble simulations with a slab-ocean coupled atmospheric global climate model (MRI-AGCM). It found a projected decrease in overall typhoon frequency but a significant increase in the annual probability of occurrence for extreme typhoons under the SSP585 future climate scenario.
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Wang et al. (2026) Canopy resistance-based modeling of maize evapotranspiration in Heilongjiang Province: a multi-site assessment integrating Sentinel-2 data and ground observations
This study developed and evaluated three canopy resistance-based evapotranspiration (λET) models within the Penman-Monteith framework, integrating Sentinel-2 data and ground observations, to improve regional farmland λET estimation for maize in Heilongjiang Province, China. The PMrc-ST model demonstrated the highest accuracy across 19 representative sites, providing valuable insights for precision agricultural water management.
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Li et al. (2026) Comprehensive assessment of six snow depth products and trends across the Qinghai-Tibet Plateau
This study comprehensively evaluated six snow depth products over the Qinghai-Tibet Plateau (QTP) to assess their ability to capture snow cover and depth. It found that the High Mountain Asia (HMA) product performed best, and revealed a general decreasing trend in snow depth and snow cover duration across the QTP from 1980 to 2023, with significant regional heterogeneity.
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Barontini et al. (2026) A parsimonius agroclimatic methodology to assess the hydrological sustainability of agriculture in the Mediterranean semiarid climate
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Falchetta et al. (2026) Street green space is relevant but not sufficient for adapting to growing urban heat in world cities
This study empirically estimates the heat stress reduction potential of street green space (SGS) across 133 cities globally using a microclimate model and a high-resolution greenness indicator, finding that while SGS expansion can offset a small percentage (2-11%) of projected urban heat increase by 2050, it is insufficient alone to adapt to growing urban heat stress.
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Li et al. (2026) Future Amplification of Moist Weather Extremes in the Midlatitudes
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Huang et al. (2026) Key Contributors to Changes in Ice Phenology and Composition in Lowland Polish Lakes During 1983–2023: Climatic Variables and Lake Morphometry
This study utilized the MyLake model with 40 years of observational data to investigate lake ice changes in Polish lowland lakes, revealing a significant decline in black ice and attributing shifts in ice phenology (delayed freeze-up, advancing break-up, thinned ice) and inter-lake differences primarily to air temperature, precipitation, and lake morphometry.
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Sun et al. (2026) Sensitivity of the 2023 Asian Summer Monsoon Water Vapor Transport to Arabian Sea Surface Temperature Anomalies
This study investigates the impact of the Arabian Sea mini warm pool (ASMWP) sea surface temperature (SST) decline rate on South Asian monsoon precipitation using an SST sensitivity experiment with the WRF model. It found that a slower-than-normal SST decline significantly increases precipitation around the ASMWP while decreasing it elsewhere, driven by changes in water vapor (evaporation, advection) and wind (pressure adjustment, convergence), ultimately enhancing convection through buoyancy changes.
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Al‐Nassar et al. (2026) On the Temporal Variability of Precipitation in Iraq: Arid‐Wet Years and Extreme Events
This study analyzes long-term precipitation variability in Iraq, distinguishing between arid and wet years and characterizing extreme events, revealing that the Indian Ocean Dipole (IOD) significantly influences interannual variability by modulating autumn-winter moisture arrival.
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Kalai et al. (2026) The Role of Daily and Monthly Bias Corrected Data in Preserving the Monthly Cross‐Correlation Between Precipitation and Temperature
This study evaluates the ability of two bias correction procedures, Canonical Correlation Analysis (CCA) and Quantile Regression (QR), to preserve the monthly cross-correlation between precipitation and maximum temperature from Global Climate Models (GCMs) over the Continental United States (CONUS). It finds that CCA outperforms QR in reproducing observed cross-correlations, and that bias correction applied at a daily temporal scale better preserves monthly cross-correlations compared to monthly bias correction.
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Trinh et al. (2026) El Nino Prediction Based on Weather Forecast and Geographical Time-series Data
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Jawad et al. (2026) Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity
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Allen et al. (2026) Characterizing Improvements in Ensemble Forecast Performance Over the Last Decade: A Retrospective Analysis of the Hydrologic Ensemble Forecast Service (HEFS)
This study retrospectively analyzes the performance of operational short- and medium-range streamflow forecasts from the California Nevada River Forecast Center (2014-2025) using a novel hierarchical Bayesian model, finding improved performance for moderate and high flow events, though improvements in ensemble spread attributes are weak.
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Roulo et al. (2026) From Global to Basin‐Scale: Identifying Best‐Performing CMIP6 Models Across Indian River Basins Through Downscaled Precipitation Products
This study introduces a novel four-level framework to evaluate and rank 24 CMIP6 Global Climate Models (GCMs) across 27 Indian River Basins using a Multi-Metric Evaluation Approach. It finds significant spatial variability in model performance and highlights that GCMs performing well for mean rainfall do not necessarily capture extreme rainfall behavior accurately.
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Liang et al. (2026) Why the statistical relationship between east asian summer monsoon rainfall and the concurrent ENSO index is weak and nonsignificant
This study explains the weak and non-significant statistical relationship between East Asian summer monsoon (EASM) rainfall and the concurrent ENSO index by demonstrating that two opposing phase relationships between the Pacific-Japan (PJ) teleconnection pattern and ENSO statistically cancel each other out. It reveals that a positive PJ pattern can be triggered by two distinct Indo-Pacific sea surface temperature (SST) anomaly patterns associated with opposite ENSO phases, each inducing different EASM rainfall responses.
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Xiang et al. (2026) Revealing the Mechanisms of Heat Extremes Using an AI Enabled Diagnostic Framework
This study introduces a globally perturbed reforecast framework driven by the Neural general circulation model (NeuralGCM) to understand the mechanisms of the record-breaking August 2022 South China heatwave (SCH22). It identifies high impact regions (HIRs) in Europe and North America whose initial condition anomalies are crucial for accurately reproducing the heatwave's evolution and spatial pattern.
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Huang et al. (2026) Why Do Extended‐Range Forecasts Underpredict the Extreme Negative Pacific/North American Pattern in February 2018?
This study investigates the failure of extended-range forecasts to capture the extreme negative Pacific/North American (PNA) event of February 2018, attributing the error to an underestimation of the East Asian trough, biases in sea surface temperatures, and complex, initialization-dependent stratospheric-tropospheric coupling.
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Zhou et al. (2026) Potential Causes of Anti‐Phase Summer Precipitation Variability Between the Middle and Lower Reaches of the Yangtze River
This study investigates the atmospheric circulation and land-ocean thermal anomaly mechanisms driving out-of-phase summer precipitation patterns in the Yangtze River basin. It identifies a non-canonical Silk Road Pattern (SLWT) maintained by specific land-ocean thermal anomalies as the key driver and proposes a new index to monitor this pattern.
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Hu et al. (2026) Enhancing ENSO Ensemble Forecast Skill by a Coupled Conditional Nonlinear Optimal Perturbation Method
This study compares two perturbation methods, Coupled Condition Nonlinear Optimal Perturbation (C-CNOP) and Singular Vector (SV), for El Niño–Southern Oscillation (ENSO) ensemble forecasting. It finds that the C-CNOP method, specifically its sea temperature component (CP-T), significantly improves ENSO forecast skill by better capturing nonlinear effects and extending skillful lead times, particularly during strong El Niño events.
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Bushra et al. (2026) Influence of Climate Drivers on Extreme Precipitation in Bangladesh: Spatiotemporal Patterns and Underlying Mechanisms
This study analyzed daily precipitation data from 1980-2017 across Bangladesh to investigate spatiotemporal trends in extreme precipitation indices (EPIs) and their linkages with large-scale climate drivers, revealing increasing dry spells and heavy rainfall frequencies alongside declining wet-day intensity, influenced by factors like ENSO and IOD.
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Dai et al. (2026) Impacts of surface heterogeneity on energy partitioning in paddy ecosystems: A dual eddy covariance study
This study quantifies the impacts of surface heterogeneity on energy partitioning in paddy ecosystems using dual eddy covariance and UAV-based footprint modeling. It reveals that even low heterogeneity significantly biases latent heat flux measurements due to advection and physiological responses, and proposes a two-stage correction framework to rectify these biases.
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Li et al. (2026) Improving CWRF Prediction of Summer Monthly Precipitation Over the Yangtze River Basin With Spatio‐Temporal Graph Neural Network
This study proposes a novel deep learning framework utilizing a spatio-temporal graph neural network (GNN) to enhance the accuracy of summer precipitation forecasts from the Climate-Weather Research and Forecasting (CWRF) model over the Yangtze River Basin (YRB). The GNN model significantly improves prediction skill across multiple metrics, outperforming existing neural network-based models.
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Wang et al. (2026) Modeling saline soil complex permittivity from bound-water microphysics: Laboratory validation at L-/C-bands
This study develops a microphysical-electromagnetic coupling model for soil complex permittivity (SCP) in saline soils, validated in the laboratory across L- and C-bands. The proposed model achieves high accuracy (R² > 0.95) and outperforms existing models, demonstrating its potential for improving microwave remote sensing of soil salinity.
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Mercier et al. (2026) Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
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Таранова et al. (2026) Статистичний Аналіз Та Індикатори Кліматичної Нестабільності Атмосферних Опадів У Тернопільській Області (1969-2024 Рр.)
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Nordling et al. (2026) First crossings of global warming levels in CMIP6 in light of observed 1.5 °C exceedance
This study quantifies the timing and likelihood of first crossing global warming levels in CMIP6 models, revealing that the observed 2024 exceedance of 1.5 °C occurred 3–7 years earlier than projected. It highlights the difficulty of avoiding a pre-mid-century 2 °C crossing even with stringent mitigation, while emphasizing the critical role of immediate mitigation in avoiding higher long-term warming.
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Tian et al. (2026) Differential response of soil moisture in surface and root zones to climate and land use changes
This study investigates the global dynamics and drivers of surface (0–10 cm) and root-zone (10–250 cm) soil moisture from 1982 to 2021, revealing significant regional differences and a stronger dominance of climate factors over land use/cover change (LUCC), particularly for root-zone soil moisture. It highlights that surface soil moisture is more sensitive to the coupling effects of LUCC and climate, while root-zone soil moisture is primarily controlled by long-term climatic conditions.
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Takami et al. (2026) Hill‐Terrain Modulation of Inland Snow‐Cloud Microphysics: Polarimetric Radar and Balloon‐Borne Particle Imaging Radiosonde Observations
This study investigated snow-cloud microphysics over inland hills in Japan, revealing that even modest terrain significantly modulates snow-particle growth and graupel formation through various atmospheric processes.
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Berner et al. (2026) Quantifying Sources of Subseasonal Prediction Skill in CESM2 Within a Perfect Modeling Framework
This study uses a perfect modeling framework with the Community Earth System Model to estimate the theoretical limit of subseasonal-to-seasonal predictability from initialization, revealing that land initialization is the dominant source of predictability over land beyond week four.
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Zhang et al. (2026) Height‐Dependent Sensitivity of Cloud Scales to Surface Temperature Anomaly Observed by Active Satellites
This study investigates how cloud horizontal scales vary with surface temperature anomalies and their impact on cloud radiative effect and precipitation, finding that cloud-scale temperature sensitivity is strongly dependent on cloud height, with significant changes in small-scale clouds driving radiative and precipitation responses.
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Ou et al. (2026) Foundation‐Scale Satellite Embeddings Reframe Hydrological Generalization as a Representation Problem
This study addresses the challenge of transferring hydrological predictive skill across time and distinct basins by introducing 64-dimensional satellite embeddings (SE) from Google's AlphaEarth Foundations model, demonstrating consistent and significant performance improvements over static-attribute baselines in Australian catchments.
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Currier et al. (2026) Scale and seasonal dependent sensitivity of hydrologic projections in the Colorado River Basin to different downscaling methods
This study compared future streamflow projections derived from dynamic (ICAR) and statistical (LOCA) downscaling methods to assess how downscaling choices impact water supply estimates. It found that while annual regional streamflow changes were not significantly different between methods, local-scale streamflow projections were more sensitive to downscaling choices due to differing seasonal and spatial precipitation and temperature patterns.
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Guo et al. (2026) Evaluating the correction methods for NDVI angular effect based on decametric resolution BRDF observations over cropland
This study comprehensively evaluated seven methods for correcting the Normalized Difference Vegetation Index (NDVI) angular effect using over 1,800 UAV-based BRDF observations and Sentinel-2 time series across seven crop types. It found that NDVI-Coupled methods, particularly the newly proposed mSVAC, significantly outperformed NDVI-Independent methods in reducing angular effects at decametric resolution.
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Liu et al. (2026) Crop height retrieval across different scenes with dual-pol backscattering and one-time in-situ data
This paper introduces a novel SAR Height Index (SHI)-based algorithm for crop height estimation that relies solely on dual-polarization SAR backscattering and a one-time in-situ calibration, demonstrating enhanced flexibility and cost-effectiveness by avoiding interferometric baseline constraints and complex parameterization. The method, validated across various crops and regions, integrates a logistic growth model to improve temporal consistency and fill data gaps, achieving high accuracy in daily crop height retrieval.
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Chen et al. (2026) Downscaling of satellite passive microwave brightness temperature through super-resolution reconstruction
This study develops an integrated land and atmospheric model approach (LEM-RTTOV) to simulate high-resolution passive microwave brightness temperature (PMW BT) and proposes a novel decoupled residual diffusion model (DRDiff) for super-resolution reconstruction to downscale satellite PMW BT, achieving accurate results for AMSR2 data.
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Yin et al. (2026) A Two‐Dimensional Deep Learning Scheme With One Predicator Only to Parametrize Global Lightning
This study introduces a novel two-dimensional artificial intelligence-based global lightning scheme utilizing convective available potential energy (CAPE) as a single predictor, achieving significant improvements in global lightning simulation performance and effectively mitigating the underestimation of extreme lightning density.
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Davies et al. (2026) Mass Addition to Timpanogos Rock Glacier: Debris‐Covered Snow and the Importance of Interannual Variability in Headwall Erosion and Climate
This study develops a probabilistic numerical model to quantify how debris deposition on snow contributes to ice accumulation in rock glaciers, regardless of permafrost presence. The model, which incorporates stochastic mountain processes, successfully reproduces observed internal stratigraphy and demonstrates that rock glaciers can add ice even in arid, warming climates.
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Martineau et al. (2026) Projected and historical amplification of moisture fluxes towards Antarctica by synoptic eddies
This study investigates projected and historical changes in moisture fluxes towards Antarctica driven by synoptic eddies using CMIP6 models and reanalysis data. It finds a significant amplification of synoptic moisture fluxes across the Antarctic Circle, primarily due to enhanced eddy moisture anomalies, with implications for Antarctic ice mass balance.
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Luo et al. (2026) Cropland biophysical impacts on land surface temperature show diurnal differences across tropical Africa
This study quantifies the diurnal biophysical impacts of cropland expansion on land surface temperature across tropical Africa, revealing consistent nighttime cooling and hydroclimatically-dependent daytime effects (cooling in arid, warming in less arid regions) driven by turbulent heat flux changes linked to leaf area index.
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Lin et al. (2026) Data-driven attribution of evapotranspiration dynamics in the Heihe River Basin: Controlling factors from site measurements to regional satellite observations
This study quantifies scale-dependent evapotranspiration (ET) dynamics and their controlling factors in the Heihe River Basin by integrating decade-long in-situ flux measurements with multi-source satellite products using an interpretable ensemble machine learning framework. It reveals that while air temperature and leaf area index are primary drivers at the site scale, regional ET patterns are dominated by climatic factors with divergent sensitivities across satellite products, emphasizing the need for scale-aware water management strategies.
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Chakraborty et al. (2026) Trends and drivers of ecosystem water use efficiency and carbon uptake modeled across South Asia
This study quantifies the spatiotemporal variability, long-term trends, and climatic drivers of gross primary productivity (GPP), evapotranspiration (ET), and ecosystem water-use efficiency (WUE) across South Asia from 1985–2023 using the Indian Land Data Assimilation System (ILDAS) with a dynamic vegetation scheme, revealing significant increases in GPP and WUE attributed to vegetation greening.
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Pellicciotti et al. (2026) DCG-MIP: the Debris-Covered Glacier melt Model Intercomparison exPeriment
This study intercompared 15 debris-covered glacier melt models across nine global sites to assess their performance in simulating ice melt under debris. It found that models with higher complexity at the atmosphere-debris interface perform best, but identified critical data gaps, particularly regarding debris thermal properties, which hinder accurate global modeling and necessitate further model development and standardized data collection.
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Olsen et al. (2026) A first approach towards dual-hemisphere sea ice reference measurements from multiple data sources repurposed for evaluation and product intercomparison of satellite altimetry
This paper introduces the Climate Change Initiative (CCI) Sea Ice Thickness (SIT) Round Robin Data Package (RRDP), a comprehensive collection of dual-hemisphere sea ice reference measurements (freeboard, thickness, draft, snow depth) from various non-satellite sources (1960-2024), repurposed and quality-controlled for evaluating and intercomparing satellite altimetry products (1993-2024).
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Liu et al. (2026) Root zone adaptive irrigation technology: A novel subsurface irrigation method using for drought-resistant afforestation in water shortage regions
This study developed and evaluated a novel root zone adaptive irrigation (RZAI) technology, demonstrating its superior efficacy in improving soil water-heat conditions and promoting seedling growth for drought-resistant afforestation across diverse arid, semi-arid, and semi-humid regions.
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Luo et al. (2026) Global net increase in surface water connectivity in river–floodplain systems
This study analyzed nearly four decades (1984–2019) of satellite observations to assess global changes in surface water connectivity across 1.6 million kilometers of river–floodplain systems, revealing a net global increase of 3% driven primarily by climatic factors and modulated by human activities.
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Nuñez-Ibarra et al. (2026) From grid to ground: how well do gridded products represent soil moisture dynamics in natural ecosystems during precipitation events?
This study evaluates four gridded soil moisture (SM) products against in situ observations from ten natural ecosystems in central and southern Chile to assess their ability to represent SM dynamics, especially during precipitation events. It finds that ERA5 and ERA5-Land generally outperform other products, particularly in humid regions, while highlighting challenges in arid areas and the diagnostic value of event-based SM signatures.
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Li et al. (2026) Physical process-based attention encoder-decoder LSTM model to improve global soil moisture prediction
This study introduces the AEDLSTM-HBV model, which integrates physical features from the Hydrologiska Byråns Vattenbalansavdelning (HBV) model into an Attention-Enhanced Encoder-Decoder Long Short-Term Memory (AEDLSTM) network to improve global soil moisture prediction. The model significantly outperforms state-of-the-art methods, particularly in permafrost and desert regions, by effectively leveraging the fusion of physical and deep learning features.
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Batool et al. (2026) Assessing seven-decade precipitation patterns using CRU, ERA5, and WorldClim with space–time cube trend detection
This study assessed seven-decade spatiotemporal precipitation patterns across Pakistan using CRU, ERA5, and WorldClim datasets with a space–time cube framework. It revealed a distinct north-south precipitation polarity, with increasing trends and intensifying hotspots in the northern and northeastern regions, contrasting with decreasing trends and persistent coldspots in the arid southwest, and identified a transitional diminishing coldspot zone in central Pakistan.
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Crowley et al. (2026) Uncertainty Analysis of Internal Hydrological Processes in Two Agricultural Catchments in Ireland via Stochastic Calibration of SWAT +
This study propagates parameter uncertainty from streamflow calibration to quantify and analyze the uncertainty of hydrological fluxes via different pathways in two Irish agricultural catchments. It identified lateral flow as a key pathway in the Bandon Catchment and agricultural tile drainage in the Owenabue Catchment, providing a more comprehensive understanding of streamflow generation and associated process uncertainty.
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Dommenget et al. (2026) Basic atmospheric dynamics control on ENSO and tropical basin interactions
This study uses a simplified atmospheric model to investigate how basic atmospheric dynamics, influenced by the shape and interaction of tropical ocean basins, control the growth rate (Bjerknes feedback) and period of the El Niño Southern Oscillation (ENSO). It finds that atmospheric processes, including an optimal zonal length for heat sources and the influence of remote basins, strongly regulate ENSO dynamics, challenging existing theories that often neglect these factors.
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Zhang et al. (2026) Characterising Rainfall‐Induced Soil Water Dynamics in Soil Profiles and Quantifying the Influencing Factors Over Mainland China
This study characterizes rainfall-induced soil water dynamics (SWD) and preferential flow (PF) patterns across soil profiles and spatial scales using in situ hourly data. It reveals novel profile patterns of SWD and PF, identifies scale-dependent environmental impacts, and elucidates complex controlling factors, providing insights for improving Earth System Models.
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Ljutic et al. (2026) Comparing Seasonal Soil Water Storage and Flow Processes Under Different Soil Conditions
This study investigated soil moisture storage and flow dynamics over two years under three agricultural management treatments (control, cover crop, compacted soil) in Southern Ontario, Canada, revealing that vertical soil moisture profiles provide critical insights into flow pathways and storage capacity often missed by total water storage analyses.
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Chu et al. (2026) An interpretable AutoML–SHAP approach for rapid urban pluvial flooding prediction
This study introduces an interpretable AutoML-SHAP framework for rapid urban pluvial flooding prediction, achieving a 2000-fold speedup over traditional hydrodynamic models while providing transparent insights into key flood drivers and their nonlinear responses.
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Rivera‐Vidal et al. (2026) Estimating the Seasonal Variations of Multiple Recharge Sources’ Contribution in Mountainous Mediterranean Basins
This study quantifies the seasonal variability of groundwater recharge sources in the Ñuble–Perquilauquén basins of Central Chile, revealing distinct seasonal dominance of diffuse, focused, and mountain-front recharge mechanisms.
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Srivastava et al. (2026) Evaluating the Impacts of Agriculture Conservation on Water Quantity and Quality Through Trend, Predictability, and Causality Analysis
This study investigated the impact of agriculture conservation practices (nature-based solutions) on watershed hydrology and water quality in Shell Creek, Nebraska, finding increased winter cover cropping correlated with reduced flood frequency and some improvements in water quality.
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Wang et al. (2026) A multi-source precipitation blending method combining hydrological model-guided precipitation adjustment and double transfer learning-based data merging
This paper introduces a novel multi-source precipitation blending method that integrates hydrological model-guided precipitation adjustment with a double transfer learning-based data merging approach to enhance precipitation data accuracy.
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Qi et al. (2026) Assessing the impact of ice thickness uncertainty on future glacier evolution in the Himalayas using a higher-order glacier flow model
This study quantifies the impact of initial ice thickness uncertainty on future glacier evolution in the Himalayas using a higher-order flowline model and seven global ice thickness datasets. It finds that by 2100, this uncertainty leads to a standard deviation of 1.5%–6.2% in projected volume loss, comparable to climate model resolution uncertainty, with greater divergence in the Central Himalayas and for smaller, lower-elevation glaciers.
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Nguyen et al. (2026) Characterizing Patterns of Drought Synchronicity in the Contiguous United States
This study investigates the regional and temporal dynamics of drought synchronicity across the contiguous United States from 1980 to 2021, revealing an increasing influence of low-frequency oscillations and higher synchronicity in the Great Plains and Midwest, particularly for short-term droughts, driven by factors including potential evapotranspiration and large-scale climate variability.
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Zhu et al. (2026) A hybrid VMD–BiLSTM–XGBoost approach for multi-scale drought forecasting in urbanizing monsoon transition zones
This study proposes a hybrid VMD–BiLSTM–XGBoost model for multi-scale meteorological drought forecasting in urbanizing monsoon transition zones. The model effectively disentangles non-stationary drought signals and adaptively captures both long-term trends and short-term extremes, demonstrating superior accuracy and reliability compared to benchmark models.
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Yu et al. (2026) Coupled Bayesian inversion of time-lapse dispersive ground penetrating radar data to estimate soil hydraulic parameters
This study introduces a coupled Bayesian inversion framework to estimate soil hydraulic parameters by analyzing time-lapse dispersive ground penetrating radar data.
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Antoniadou et al. (2026) A Bayesian spatial framework for modeling sub-hourly to daily extreme precipitation in Denmark using SPDE with INLA
This study introduces a new Bayesian spatial framework for modeling sub-hourly to daily extreme precipitation in Denmark, generating spatially continuous return level maps with associated uncertainties. The two-stage model, utilizing Negative Binomial and Generalized Pareto distributions with latent spatial random effects, captures spatial variation in extreme event frequency and magnitude, showing improved performance over the existing national model for shorter durations.
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Khemiri et al. (2026) The Superposition of Climate Change and Anthropogenic Pressure Threatens Blue Water Resources in Tunisia
This study assesses the combined impacts of climate change, land use/land cover change, and irrigation on surface and groundwater in a Tunisian agro-hydrological basin. It identifies critical hydrological thresholds for irrigation-induced groundwater recharge and projects future reservoir decline, threatening water sustainability.
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Yang et al. (2026) Combined effects of arctic thermal conditions and ENSO on Eurasian winter temperature extremes
This study investigates the nonlinear combined effects of Arctic (Barents-Kara Seas) thermal conditions and ENSO on Eurasian winter mean and extreme temperatures, identifying tropospheric wave trains as the primary underlying mechanism. The findings highlight that specific Arctic-ENSO combinations lead to distinct Eurasian temperature responses, with significant implications for predicting cold extremes.
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Patricola et al. (2026) Correction: Mid-twenty-first century climate change in the Central United States. Part II: Climate change processes
This document is a correction notice for a previously published article titled "Mid-twenty-first century climate change in the Central United States. Part II: Climate change processes," primarily correcting an author's name and email address. The core objective and main findings of the original paper are not detailed in this correction.
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Tan et al. (2026) Observational characteristics of cloud-radiation-precipitation during 2019 drought period in Yunnan of Southwest China
This study analyzes cloud-radiation-precipitation changes during the extreme drought period from April to June 2019 in Yunnan, Southwest China, revealing that anomalous subsidence and reduced water vapor transport led to decreased cloud cover and a weakened cloud radiative cooling effect, intensifying and sustaining the drought through a "hot-dry" feedback cycle.
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Deng et al. (2026) A generalized enhanced snow/ice index based on Landsat imagery for accurate snow/ice mapping in mountainous areas
This paper proposes a generalized enhanced snow/ice index derived from Landsat imagery to improve the accuracy of snow and ice mapping, specifically targeting mountainous regions.
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Li et al. (2026) The combined impacts of NAO and ENSO on the surface air temperature anomalies in boreal winter
This study investigates the combined impacts of the North Atlantic Oscillation (NAO) and the El Niño–Southern Oscillation (ENSO) on boreal winter surface air temperature (SAT) anomalies using reanalysis and numerical simulations. It reveals that the combined effects exhibit nonlinear characteristics, with ENSO modulating NAO-induced circulation anomalies through distinct Rossby wave trains, thereby amplifying or weakening regional SAT anomalies.
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Yamaguchi et al. (2026) Dynamical and Radiative Influence on the Hadley Circulation by Aerosol‐Cloud Interactions
This study investigates how aerosols influence large-scale atmospheric circulation and the cloud radiative effect (CRE) using a 2D Hadley circulation model, revealing that aerosols intensify circulation and brighten clouds, but this effect is significantly weaker when coupled with a slab ocean model, indicating a moderating role of the Hadley circulation in a dynamically coupled atmosphere-ocean system.
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Naeini et al. (2026) Projecting Hurricane Risk in Atlantic Canada under Climate Change
This study projects future tropical cyclone (TC) risk in Atlantic Canada under climate change, quantifying the evolution of wind and coastal flood hazards and associated economic losses. Findings indicate an intensification of wind extremes and substantial coastal inundation amplification, leading to higher wind-proxy risk for many coastal communities.
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Tien et al. (2026) Evaluation of Seasonal Precipitation Forecasts over the Upper Tigris–Euphrates Basin
This study assesses the performance of seasonal precipitation forecasts for the upper Tigris–Euphrates basin, revealing that statistical models based on atmospheric–oceanic indices, particularly a hybrid model, outperform complex dynamic models from the North American Multi-Model Ensemble.
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Liu et al. (2026) A hybrid machine learning and optimal stomatal behavior model to reveal the role of vegetation dynamics in potential evapotranspiration and drought
This study develops a hybrid machine learning and optimal stomatal behavior model to investigate the influence of vegetation dynamics on potential evapotranspiration and drought conditions.
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Jeong et al. (2026) Atmospheric circulation and moisture budget drivers of dominant modes of winter extreme precipitation variability over North America
This study identifies three dominant modes of winter extreme precipitation variability (monthly maximum consecutive 5-day precipitation, Rx5day) across North America, explaining 30.7% of total variance, and attributes them to large-scale atmospheric circulation and lower-tropospheric wind-driven moisture transport.
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Lung et al. (2026) The Influence of Open Boundary Conditions and Model Resolution on Shallow Cloud Organization in Atmospheric Large Eddy Simulations
This study presents and compares an open boundary, one-way nested high-resolution Large Eddy Simulation (LES) with a periodic LES, both forced by the HARMONIE-AROME regional weather model, to evaluate their representation of cloud structures. It finds that the open boundary LES, by inheriting the full atmospheric state, maintains more constant, larger, and organized clouds compared to the periodic LES, which exhibits stronger daily cycles and intermittent cloud behavior.
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Audette et al. (2026) Numerical Water Tracers in the Atmospheric Component of the Energy Exascale Earth System Model: Implementation and Changes in Moisture Origin
This study implements numerical water tracers in the E3SMv2 model to investigate changes in the global hydrologic cycle under future climate scenarios, revealing increased moisture export from mid-latitude and southern subtropical regions and enhanced local evaporation in polar areas. A novel statistical reconstruction method for water vapor origin is also proposed to reduce computational cost.
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Ventura et al. (2026) Optimizing canopy cover evaluation: A machine learning approach using LiDAR data
This study develops AI-CanopyMapper, a machine learning framework leveraging LiDAR data for efficient and accurate prediction of canopy cover, achieving a mean absolute error of 6.47% and an R² of 0.88 for the full model in Catalonia. The framework demonstrates strong generalization capabilities and computational efficiency, even with limited data, offering a fast and scalable alternative to traditional methods.
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Li et al. (2026) A physically based neural network for flood routing: The Muskingum-Recurrent neural network
This study develops a Muskingum-Recurrent Neural Network (MRNN) that integrates the Muskingum flood routing equations directly into the RNN architecture, enforcing mass conservation as a hard constraint. The MRNN demonstrates superior data efficiency, robustness, and physical consistency in flood routing compared to conventional neural networks and traditional process-based methods across artificial, benchmark, and real-world flood events.
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Li et al. (2026) Anthropogenic Forcing Amplifies Concurrent Risk of Pluvial Pakistan–Hot Yangtze
This study quantifies the role of anthropogenic forcing in driving trans-regional concurrent extreme events, specifically the 2022 Pakistan floods and Yangtze River Basin heatwaves, finding that anthropogenic forcing accounts for nearly 100% of the likelihood of such events, with their probability projected to increase significantly by the end of the century.
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Wang et al. (2026) A novel temporal distribution smoothing alignment network for water level interval forecasting in hydropower systems
This paper introduces a novel temporal distribution smoothing alignment network designed to improve water level interval forecasting in hydropower systems.
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Tang et al. (2026) Unprecedented 2024 East Antarctic winter heatwave driven by polar vortex weakening and amplified by anthropogenic warming
This study investigates the unprecedented July-August 2024 East Antarctic winter heatwave, identifying polar vortex weakening as the primary driver and quantifying a significant amplification by anthropogenic warming, which increased its likelihood by more than twofold.
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Men et al. (2026) The global response patterns of diurnal temperature range to soil moisture under different climatic backgrounds
This study investigates the global response patterns of diurnal temperature range (DTR) to soil moisture (SM) variations across different climatic backgrounds from 1980 to 2022, revealing a significant, often nonlinear, negative correlation where DTR is more sensitive to SM changes under low SM conditions.
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Fan et al. (2026) When the Past Matters: How Model Initialization Can Lead to Surprises in Long‐Term Simulations in Glaciated Environments
This paper highlights that improper initialization of surface and subsurface water storage and the inability of hydrological models to account for landscape evolution lead to significant errors in long-term distributed streamflow simulations, recommending long exploratory runs to steady state.
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Verjans et al. (2026) Large potential of performance-based model weighting to improve decadal climate forecast skill
This study implements a performance-based model weighting scheme for decadal climate predictions, focusing on sea-surface temperature, demonstrating its potential to improve forecast skill, particularly when predicting pseudo-observations, but revealing challenges in validating these gains against real-world observations.
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Huang et al. (2026) Global hotspots of compound extreme heat-pollution linked to local surface and atmospheric conditions
This study provides a global assessment of compound extreme heat and particulate matter (PM2.5) pollution events from 2003 to 2020, identifying Sub-Saharan Africa and the Indus River Valley as hotspots and linking these events to specific local surface and atmospheric conditions.
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Türk et al. (2026) Tracking Event‐Scale Precipitation Partitioning Reveals Comparable Roles of Event Characteristics and Seasonality in Shaping Precipitation Fate in a Forested Landscape
This study investigated how precipitation event characteristics and seasonality influence the partitioning of precipitation into streamflow and evapotranspiration at the event scale over a 1-year tracking period. It found that event characteristics play an equally important role as seasonality in determining the fate of precipitation, with summer/spring precipitation returning to the atmosphere faster and in greater proportion than autumn/winter precipitation.
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Patricola et al. (2026) Correction: Sub-Saharan Northern African climate at the end of the twenty-first century: forcing factors and climate change processes
This document is a correction notice for a previously published article, primarily updating an author's name from Christina M. Patricola to Christina M. Patricola-DiRosario and her email address.
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Zhou et al. (2026) Predicting the unprecedented: assessing contributions from large-scale modes of variability and climate change to Southeast Australia’s record spring rainfall in 2022
This study quantifies the contributions of large-scale climate drivers and anthropogenic global warming to Southeast Australia's record spring 2022 rainfall. It reveals that while these factors explained a substantial portion, local atmospheric conditions and an increased frequency of intense weather systems played a critical role in amplifying the event's unprecedented extremity, with anthropogenic climate change contributing approximately 12% to the total rainfall.
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Patricola et al. (2026) Correction: Northern African climate at the end of the twenty-first century: an integrated application of regional and global climate models
The provided text is a correction notice for an original article, primarily addressing an author's name misspelling and an email address update. It does not contain a summary of the original paper's core objective or main finding.
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Yaqoob et al. (2026) Variable Rate Irrigation Through Digital Agriculture for Sustainable Water Management: A Meta Review on Current Challenges and Future Directions
This meta-review synthesizes advancements, challenges, and future directions in Variable Rate Irrigation (VRI) systems, integrating digital agriculture technologies like AI, ML, and smart sensing for sustainable water management. It highlights VRI's potential to optimize water use, increase crop yield, and reduce greenhouse gas emissions by addressing spatial variability in agricultural fields.
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Neto et al. (2026) Hydroclimatic Variability Shapes Long‐Term Water Balance
This study demonstrates that sub-annual hydroclimatic variability, including seasonal covariance, monthly variance, and event-scale storm structure, significantly influences long-term water balance, proposing an expanded aridity framework to explicitly integrate these factors into water-balance theory.
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Dykman et al. (2026) Annual Streamflow and Flood Event Simulation for Future Water Supply—A Multiple Lines of Evidence Approach
This study investigates a multiple-lines-of-evidence approach to reduce uncertainty in streamflow projections, particularly for extreme flood events, by comparing regional climate model (RCM) downscaling with a continuous precipitation generation approach. It finds that continuous simulation can offer more reliable and computationally efficient inputs for water resource planning, especially in wetter regions, by producing lower biases in modeled streamflow compared to RCM downscaling.
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Gampe et al. (2026) The emergence of snow droughts as drivers of negative extremes in plant productivity over the past decades.
This study quantifies the significant and increasing impact of snow droughts as drivers of negative gross primary production (GPP) anomalies across the Northern Hemisphere, revealing their prominent role in the global carbon cycle.
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Jahangir et al. (2026) A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction
This study introduces a novel hybrid Long Short-Term Memory (LSTM) and Random Forest (RF) fine-tuning method that significantly accelerates and enhances deep learning model development for streamflow prediction, demonstrating superior efficiency and accuracy compared to conventional methods.
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Nasseri et al. (2026) Exploring Accuracy and Uncertainty in Watershed-Scale Estimation of Actual Evapotranspiration: Comparing Conceptual Budyko Framework and Machine Learning Methods
This study compared Budyko-like conceptual frameworks with Random Forest and XGBoost machine learning models for actual evapotranspiration (Eₐ) estimation across 598 sub-basins in Iran. Machine learning models significantly outperformed conceptual approaches in accuracy and robustness, with dryness index and basin slope identified as dominant controls, while also providing more comprehensive uncertainty quantification.
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Guo et al. (2026) Quantitative Research on the Interaction Relationship between Water and Land Resources Based on the Binary Water Cycle
This study quantitatively analyzes the dynamic feedback between water and land resources in Luoyang City, China, using a "natural-social" binary water cycle framework, finding that cultivated land expansion negatively impacts available water long-term, while precipitation is the primary positive determinant.
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Neelam (2026) Global analysis of watershed characteristics modulating the sensitivity of evapotranspiration to heat and dry extremes
This study investigates how Climate Extreme Indices (CEIs) affect fractional evapotranspiration (fET) across global watersheds, specifically quantifying the modulating role of watershed characteristics. It reveals that soil texture and topography distinctly influence fET responses to dry events, while different heatwave metrics provide nuanced insights into vegetation stress.
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Zhao et al. (2026) Exploration on Coupling Machine Learning with Hydrological Model to Enhance Runoff Simulation
This study investigates how coupling process-driven hydrological models with varying physical mechanisms with a Long Short-Term Memory (LSTM) model, and introducing a Stacking structure, impacts runoff simulation accuracy and robustness in the Yalong River Basin. It demonstrates that models with stronger physical mechanisms enhance coupling performance, and the Stacking structure significantly improves simulation stability and consistency.
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Moradi et al. (2026) Evaluating Design Peak Flow Estimation Methods
This study evaluates four hydrological approaches for design peak flow estimation across five sub-basins of the Gidra River in Slovakia for return periods of 2–1000 years. It finds that the STORAGE + COSMO4SUB (S + C) model, particularly with the Pearson Type III distribution, provides more consistent and balanced results compared to empirical methods, especially in ungauged basins, though uncertainties remain for extreme events.
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Largeau et al. (2026) Investigating the robustness of extreme precipitation super-resolution across climates
This study introduces a novel framework for super-resolving the Generalized Extreme Value (GEV) distribution parameters of hourly precipitation extremes using Vector Generalized Additive Models (VGAMs) and Vector Generalized Linear Models (VGLMs). It quantifies model robustness to climate change via a "robustness gap" and identifies limits to super-resolution factors based on spatial correlations.
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Dong et al. (2026) Complex-terrain correction of land surface temperature using a temporal–spatial coupling data-driven model
This paper introduces a novel temporal–spatial coupling data-driven model designed to improve the accuracy of land surface temperature (LST) measurements in complex terrain by correcting for associated errors.
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Laluet et al. (2026) Assessing the suitability of global evapotranspiration products over irrigated areas
This study evaluates the suitability of six global evapotranspiration (ET) products over irrigated croplands by comparing their spatial patterns, seasonal dynamics, and magnitudes against irrigation maps, an independent ET ensemble, and eddy covariance measurements across diverse agro-climatic regions. The assessment reveals significant differences, with PMLv2, SSEBop v6.1, and FLUXCOM RS generally showing the strongest and most consistent agreement with reference datasets, while ERA5-Land exhibits the weakest correspondence.
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Liu et al. (2026) Predicting Lake Surface Water Temperature With Transfer‐Based Physics‐Informed Deep Learning
This study introduces Transfer-PIDL, a transfer learning framework, to enhance the generalizability of physics-informed deep learning (PIDL) for lake surface temperature prediction, demonstrating superior accuracy and reduced data requirements across diverse lakes.
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Li et al. (2026) Dryland dominance in the slowdown of global vegetation carbon uptake
This study reveals an asymmetric slowdown in global vegetation carbon uptake, dominated by drylands since 2001 due to water constraints from rising vapor pressure deficit, while humid regions maintain increased uptake. Current global vegetation and Earth system models fail to capture this divergence, indicating a potential limitation to the future land carbon sink.
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Ghaneei et al. (2026) The Role of Baseflow Data Assimilation in Hydrologic Modeling and Peak Flow Prediction
This study applies a Hydrologic Generative Ensemble Data Assimilation method to merge observed baseflow data with hydrologic model outputs, updating lower-zone water storage states across the eastern U.S. The assimilation significantly shifts runoff partitioning towards higher baseflow contributions, leading to improved characterization of the full hydrograph and more accurate peak flow detection without altering the model structure.
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Monerie et al. (2026) Global wind stilling and the role of sub-monthly variability in explaining deficiencies in atmospheric reanalyses
This study confirms the robustness of global wind stilling (1980-2010) in Northern Hemisphere land observations and reveals that most atmospheric reanalyses fail to reproduce this trend, primarily due to their inability to capture changes in sub-monthly wind speed variability. It also highlights the significant implications for wind power density assessments and reconciles contradictory findings in previous literature based on data processing methods.
