Li et al. (2026) Robustness and uncertainty in drought trend projections across dryland and non-dryland regions under greenhouse warming

Identification

• Journal: Weather and Climate Extremes
• Year: 2026
• Date: 2026-06-14
• Authors: Linchao Li, Qinsi He, Yu Shi, Puyu Feng, Limin Duan, Lijie Shi, Kai Ren, Ning Yao, Shang Chen, Ke Liu, Matthew Tom Harrison, Yi Li, De Li Liu, ALFREDO R. HUETE, Qiang Yu, Guijun Yang
• DOI: 10.1016/j.wace.2026.100924

Research Groups

• College of Agronomy, Inner Mongolia Agricultural University
• State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University
• School of Life Sciences, Faculty of Science, University of Technology Sydney
• Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University
• College of Land Science and Technology, China Agricultural University
• College of Hydraulic Science and Engineering, Yangzhou University
• State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University
• College of Water Resources and Architectural Engineering, Northwest A&F University
• Tasmanian Institute of Agriculture, University of Tasmania
• NSW Department of Primary Industries, Wagga Wagga Agricultural Institute
• Climate Change Research Centre, University of New South Wales
• College of Geological Engineering and Geomatics, Chang'an University
• Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences

Short Summary

This study projects drought trends using the Standardized Precipitation Evapotranspiration Index (SPEI) and Aridity Index (AI) under different greenhouse gas emission scenarios. The results show that future drying signals differ systematically between dryland and non-dryland regions depending on the drought metric used.

Objective

• Project drought characteristics using different indices and a multi-model ensemble under various scenarios.
• Reveal temporal and spatial patterns of drought and thus identify regions that are becoming drier or wetter.
• Explore the sources of uncertainty in drought trend projections.

Study Configuration

• Spatial Scale: Global, with a focus on dryland and non-dryland regions.
• Temporal Scale: 1930-2099, with a historical period (1930-2014) and future projection period (2015-2099).

Methodology and Data

• Models used: 36 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project (CMIP6).
• Data sources: CMIP6 data, with a focus on precipitation and temperature trends.

Main Results

• The SPEI trend showed limited change during the historical period and under SSP126.
• Negative trends occurred in northern Africa, which are even more pronounced during the historical period than those in the future period under SSP126.
• Approximately 46% and 65% of areas exhibit SPEI trends lower than −0.1 per decade under SSP245 and SSP585, respectively.
• The AI trend projections showed decreasing trends in most regions under SSP245 and SSP585.

Contributions

This study provides a comprehensive assessment of drought evolution under climate change using multiple indices and a large ensemble of GCMs. The results highlight the importance of considering both region type and drought metric when projecting future drought trends.

Funding

• This research was funded by the National Natural Science Foundation of China (Grant No. 41877052).
• The authors also acknowledge funding from the Australian Research Council (Grant No. DP180100650).

Citation

@article{Li2026Robustness,
  author = {Li, Linchao and He, Qinsi and Shi, Yu and Feng, Puyu and Duan, Limin and Shi, Lijie and Ren, Kai and Yao, Ning and Chen, Shang and Liu, Ke and Harrison, Matthew Tom and Li, Yi and Liu, De Li and HUETE, ALFREDO R. and Yu, Qiang and Yang, Guijun},
  title = {Robustness and uncertainty in drought trend projections across dryland and non-dryland regions under greenhouse warming},
  journal = {Weather and Climate Extremes},
  year = {2026},
  doi = {10.1016/j.wace.2026.100924},
  url = {https://doi.org/10.1016/j.wace.2026.100924}
}