Su et al. (2026) Precipitation observing network gaps limit climate change impact assessment

Identification

• Journal: Nature
• Year: 2026
• Date: 2026-03-25
• Authors: Jiajia Su, Chiyuan MIAO, Francis W. Zwiers, Hylke Beck, Phil Jones, Qiaohong Sun, Louise J. Slater, Wouter R. Berghuijs, Yoshihide Wada, Daniel Rosenfeld, Jiaojiao Gou, Yi Wu, Paolo Tarolli, Pasquale Borrelli, Panos Panagos, Lisa V. Alexander, Qi Zhang, Jinlong Hu, Seung-Ki Min, Luis Samaniego, Qingyun Duan, Georgia Destouni, Jose A. Marengo, Reza Modarres, Soroosh Sorooshian
• DOI: 10.1038/s41586-026-10300-5

Research Groups

• State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, China
• State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
• Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada
• Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
• Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, UK
• School of Geography and the Environment, University of Oxford, Oxford, UK
• Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
• Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Makkah, Saudi Arabia
• Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
• Department of Land, Environment, Agriculture and Forestry, Università degli Studi di Padova, Legnaro, Italy
• Environmental Modeling and Global Change Lab, Department of Science, Roma Tre University, Rome, Italy
• Department of Environmental Sciences, Environmental Geosciences, University of Basel, Basel, Switzerland
• European Commission, Joint Research Centre (JRC), Ispra, Italy
• Climate Change Research Centre and ARC Centre of Excellence for Weather of the 21st Century, UNSW Sydney, Sydney, Australia
• Qinghai Province Key Laboratory of Physical Geography and Environmental Process, College of Geographical Science, Qinghai Normal University, Xining, China
• Division of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea
• Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
• Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
• State Key Laboratory of Water Disaster Prevention, College of Hydrology and Water Resources, Hohai University, Nanjing, China
• Department of Physical Geography, Stockholm University, Stockholm, Sweden
• Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
• Stellenbosch Institute for Advanced Study, Stellenbosch, South Africa
• Centro Nacional de Monitoramento e Alertas de Desastres Naturais, CEMADEN, São José dos Campos, Brazil
• UNESP/CEMADEN, São José dos Campos, Brazil
• Graduate School of International Studies, Korea University, Seoul, South Korea
• Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran
• Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, USA

Short Summary

This study evaluates the global distribution of 221,483 precipitation gauges and identifies priority regions for network expansion under historical and future climate/socioeconomic scenarios. It finds that only 13.4% of the global land surface meets WMO monitoring requirements, with 25% currently needing urgent expansion, increasing to 32.1% under a high-emission scenario when socioeconomic vulnerabilities are considered.

Objective

• To comprehensively evaluate the global distribution and density of precipitation gauge networks against World Meteorological Organization (WMO) standards.
• To identify priority regions worldwide where new precipitation gauges are most needed, considering historical precipitation characteristics, projected future precipitation changes, and anticipated socioeconomic conditions under different emission scenarios.

Study Configuration

• Spatial Scale: Global land surface, analyzed on a 1° x 1° grid, with sensitivity tests at 0.5°, 2.5°, and 5° resolutions. Physiographic regions include Interior Plains, Hilly/Undulating, Mountains, Coastal Zones, Small Islands, Urban Areas, and Polar/Arid regions.
• Temporal Scale:
  • Historical Gauge Records: 1900–2022 for precipitation gauge distribution and density.
  • CMIP6 Historical Simulations: 1970–1999 for climate variable analysis.
  • Future Projections: 2025–2100 for precipitation trends and socioeconomic conditions under low-emission (SSP1-2.6) and high-emission (SSP5-8.5) scenarios.

Methodology and Data

• Models used:
  • Maximum Information Minimum Redundancy (MIMR) criterion: Quantifies the informational value and redundancy of precipitation observations.
  • Random Forest (RF) model: Trained on existing gauge locations to estimate future precipitation trends and quantify projection uncertainty (Absolute Error, AE).
  • Principal Component Analysis (PCA) and Entropy Weight Method (EWM): Objective weighting methods used to combine multiple criteria (gauge density, MIMR, AE, population density, GDP) into a single Priority for Siting New Gauges (PSNG) score.
• Data sources:
  • Precipitation Gauge Network: A compiled dataset of 221,483 unique daily precipitation gauges from 15 global and national sources (1900–2022). A subset of 38,203 Long-term Record (LR) stations (records > 30 years, < 10% missing data) was used for spatial variability analysis.
  • CMIP6 Model Data: Monthly climate variables (precipitation flux, near-surface air temperature, evapotranspiration, relative humidity, black carbon aerosol mass emission rate) from 13 models for historical (1850–2014) and future (2015–2100) periods under SSP1-2.6 and SSP5-8.5.
  • Socioeconomic Data: Global gridded population density (person per square kilometer) and GDP (in 2005 US dollars, purchasing power parity) for 2025–2100 under SSP1 and SSP5 scenarios.
  • Physiographic Maps: Global Ecological Land Units (GELU) database (250-meter resolution), Global, Self-consistent, Hierarchical, High-resolution Shoreline (GSHHS) database, and Global Islands Database (GID).

Main Results

• Only 13.4% of the global land surface currently meets the WMO requirements for annual precipitation monitoring, dropping to 1.9% for long-term record (LR) stations, indicating widespread scarcity.
• Europe exhibits the highest continental gauge density (2.4 gauges per 1,000 square kilometers), with Germany leading among countries larger than 50,000 square kilometers (22.4 gauges per 1,000 square kilometers).
• Based on historical precipitation information (1900–2022), approximately 25% of global land areas are identified as high priority (PSNG score 7–8) for siting new gauges, particularly in Central Africa, northern South America, northern North America, and southern Asia.
• Under a high-emission scenario (SSP5-8.5) that incorporates future precipitation projections and socioeconomic conditions (2025–2100), the proportion of high-priority land increases to 32.1% globally.
• Low-latitude regions in Africa (54.9% of land area) and South America (39.3% of land area) show the most extensive coverage of high PSNG under SSP5-8.5, with increases of 16.3% and 7.0% respectively compared to the historical period.
• Socioeconomic factors significantly amplify PSNG in Europe (43.6% increase), Asia (31.4% increase), and North America (14.4% increase) under SSP5-8.5, particularly in high-population or high-GDP nations.
• Urban regions emerge as a critical physiographic area where more than half of sub-regions rank as high PSNG under future scenarios, underscoring the need for fine-scale monitoring in densely populated areas.

Contributions

• Provides the first comprehensive global assessment of precipitation gauge network deficiencies by integrating historical observations, future climate projections, and socioeconomic vulnerabilities.
• Develops a novel, multi-criteria framework (PSNG) to objectively identify and prioritize specific regions for new gauge deployment, offering actionable guidance for climate monitoring infrastructure investment.
• Quantifies the extent to which global precipitation monitoring falls short of WMO standards and highlights the critical implications for climate change impact assessment and water resource management.
• Demonstrates how future climate variability and socioeconomic pressures will shift and intensify the need for enhanced precipitation monitoring, particularly in vulnerable and rapidly developing regions.
• Emphasizes the urgent need for strategic investments in new gauges and improved open data access to enhance climate resilience and advance understanding of the global water cycle.

Funding

• National Natural Science Foundation of China (42521001, U24A20572)
• National Key Research and Development Program of China (2024YFF0809301)
• 111 Project of China (B23027)
• Australian Research Council (ARC) grants FT210100459 and CE230100012
• Swedish Research Council (VR, project grant 2022-04672)

Citation

@article{Su2026Precipitation,
  author = {Su, Jiajia and MIAO, Chiyuan and Zwiers, Francis W. and Beck, Hylke and Jones, Phil and Sun, Qiaohong and Slater, Louise J. and Berghuijs, Wouter R. and Wada, Yoshihide and Rosenfeld, Daniel and Gou, Jiaojiao and Wu, Yi and Tarolli, Paolo and Borrelli, Pasquale and Panagos, Panos and Alexander, Lisa V. and Zhang, Qi and Hu, Jinlong and Min, Seung-Ki and Samaniego, Luis and Duan, Qingyun and Destouni, Georgia and Marengo, Jose A. and Modarres, Reza and Sorooshian, Soroosh},
  title = {Precipitation observing network gaps limit climate change impact assessment},
  journal = {Nature},
  year = {2026},
  doi = {10.1038/s41586-026-10300-5},
  url = {https://doi.org/10.1038/s41586-026-10300-5}
}