Zhou et al. (2026) What drives the compound drought and heat events? A dynamic perspective

Identification

• Journal: Weather and Climate Extremes
• Year: 2026
• Date: 2026-01-29
• Authors: S. Kevin Zhou, Shaohong Wu, Jiangbo Gao, Lulu Liu, Jie Wang, Rui Yan, Bingyan Li
• DOI: 10.1016/j.wace.2026.100863

Research Groups

• Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
• University of Chinese Academy of Sciences, Beijing, 100049, China

Short Summary

This study develops a novel dynamic framework integrating Meta-Gaussian models, differential equations, and random forest regression to characterize compound drought and heat events (CDHE), attribute key drivers, and identify regional disparities. Findings reveal a significant global increase in CDHE frequency and intensity from 1960 to 2022, with a dynamic shift in dominant drivers from heat to drought conditions and their interaction under climate warming, varying distinctly across arid and humid regions.

Objective

• Analyzing the spatiotemporal variations of the standardized temperature index (STI), the standardized precipitation evapotranspiration index (SPEI), and their correlation (R).
• Identifying the characteristics of CDHE intensity and joint return periods.
• Quantifying the dynamic impacts of driving factors on CDHE across different wetness zones.

Study Configuration

• Spatial Scale: Global land areas between 60°N and 60°S, excluding deserts (total annual precipitation below 100 mm). Data resolution: 0.5° x 0.5°.
• Temporal Scale: Monthly data from 1960 to 2022. Analysis uses 30-year sliding windows.

Methodology and Data

• Models used:
  • Meta-Gaussian model: For characterizing dependence structures, estimating joint return periods, and theoretical sensitivity analysis (via differential equations).
  • Modified Mann-Kendall (MK) test with variance correction: For long-term trend analysis.
  • Sen's slope estimator: For quantifying the magnitude of linear trends.
  • Random Forest Regression: For quantifying nonlinear dynamic importance of driving factors and their temporal evolution.
  • Pearson correlation: To determine the direction of influence for driving factors.
• Data sources:
  • Climatic Research Unit (CRU TS v4) at the University of East Anglia: Monthly mean temperature, precipitation, and potential evapotranspiration (PET) data from 1960 to 2022, at 0.5° x 0.5° resolution.
  • Aridity Index (AI) dataset: From Zomer et al. (2022).
  • Derived indices: Standardized Temperature Index (STI) and Standardized Precipitation Evapotranspiration Index (SPEI) at a 1-month timescale.

Main Results

• The frequency and intensity of CDHE significantly increased globally from 1960 to 2022. The return period of CDHE shortened in 77.60% of the area, with a global average change rate of -0.30.
• The increase in STI-dominated CDHEs (growth rate of 25,113.85) marginally exceeded that of SPEI-dominated CDHEs (growth rate of 16,929.99), highlighting heat as a critical driving factor.
• Attribution analysis revealed that the Standardized Temperature Index (STI) is the dominant factor influencing CDHE, accounting for approximately 67% of the global area. The Standardized Precipitation Evapotranspiration Index (SPEI) accounts for 23%, and the correlation (R) between STI and SPEI accounts for 10%.
• Under climate warming, the relative influence of STI on CDHE occurrence diminishes, while the importance of SPEI and R (drought conditions and their interaction with heat) progressively increases, emerging as primary factors.
• Regional variations show that in arid regions, CDHE is more sensitive to changes in SPEI (drought conditions), with the most pronounced increasing trend in SPEI importance (0.017). In humid regions, STI (heat) plays a predominant role, with the greatest decreasing trend in STI importance (-0.010).

Contributions

• Developed a novel dynamic framework integrating Meta-Gaussian models, differential equations, and random forest regression for a comprehensive and dynamic analysis of CDHE drivers.
• Provided a dynamic, long-term perspective on CDHE drivers, revealing a shift in dominant factors from heat to drought and its interaction under climate warming, which was previously unclear from static analyses.
• Elucidated regional disparities in CDHE driving mechanisms, identifying distinct dominant factors in arid versus humid regions based on land-atmosphere feedbacks and soil moisture availability.
• Advanced the understanding of CDHE dynamics and provided critical insights for enhancing predictive models and informing differentiated, proactive climate adaptation strategies.

Funding

• The National Key Research and Development Program of China, Grant/Award Number: 2023YFF0805704
• The National Natural Science Foundation of China, Grant/Award Number: 42371084, 42101311

Citation

@article{Zhou2026What,
  author = {Zhou, S. Kevin and Wu, Shaohong and Gao, Jiangbo and Liu, Lulu and Wang, Jie and Yan, Rui and Li, Bingyan},
  title = {What drives the compound drought and heat events? A dynamic perspective},
  journal = {Weather and Climate Extremes},
  year = {2026},
  doi = {10.1016/j.wace.2026.100863},
  url = {https://doi.org/10.1016/j.wace.2026.100863}
}