Huang et al. (2025) Differential sensitivities of three types of compound drought and heatwave events to human-induced climate change across the globe

Identification

• Journal: Weather and Climate Extremes
• Year: 2025
• Date: 2025-11-20
• Authors: Shuzhe Huang, Siqi Wang, Chao Wang, Xiang Zhang, Jianya Gong, Nengcheng Chen
• DOI: 10.1016/j.wace.2025.100836

Research Groups

• State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
• National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, China
• School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
• Hubei Luojia Laboratory, Wuhan, China

Short Summary

This study quantifies the differential influences of human-induced climate change on three types of compound drought and heatwave (CDHW) events (precipitation-based, runoff-based, and soil-moisture-based) using CMIP6 simulations, revealing greenhouse gas forcing as the dominant driver of global CDHW intensification, particularly for soil-moisture-based events, and projecting significant future severity growth and population exposure under high-emission pathways.

Objective

• To quantify how human-induced climate change has altered the characteristics of precipitation-based (CMDH), runoff-based (CHDH), and soil-moisture-based (CSDH) compound drought and heatwave events during the historical period (1960–2014), and how these anthropogenic signals differ across event types, regions, and seasons.
• To project how these three CDHW types are expected to evolve under future climate scenarios (2015–2100) and assess the implications for global population exposure.

Study Configuration

• Spatial Scale: Global, with outputs regridded to a 1° × 1° resolution. Regional analysis based on K¨oppen-Geiger climate classifications and IPCC Sixth Assessment Report (AR6) reference regions.
• Temporal Scale: Historical attribution period: 1960–2014. Future projection period: 2015–2100.

Methodology and Data

• Models used: Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations.
• Data sources:
  • CMIP6 daily precipitation (pr), maximum temperature (tasmax), surface runoff (mrro), soil moisture (mrsos), relative humidity (hurs), leaf area index (lai), and minimum temperature (tasmin) for historical (all-forcing, greenhouse gas-only, aerosol-only, natural-only) and future (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) scenarios.
  • K¨oppen-Geiger climate classifications.
  • IPCC-AR6 reference regions.
  • Annual population distribution projections (2015–2100) consistent with SSP scenarios.
  • ERA5-Land reanalysis data for validation.

Main Results

• From 1960 to 2014, approximately 98.8%, 97.5%, and 97.0% of land areas showed increasing frequency for CMDH, CHDH, and CSDH, respectively, while 74.0%, 76.3%, and 75.9% of land areas exhibited intensification in severity. CSDH showed the strongest multi-decadal increases in both frequency (trend slope 0.088, p < 0.05) and severity (trend slope 0.032, p < 0.05).
• Greenhouse gas (GHG) forcing is the dominant driver of global CDHW intensification, with CSDH showing the strongest and most coherent amplification of frequency and severity. Aerosol forcing partially offset these increases, particularly in monsoon regions.
• Under hist-GHG forcing, CSDH showed the most coherent strengthening across IPCC-AR6 regions, with 59.09% of regions showing joint rises in frequency and severity.
• Return periods for CDHW events exceeding the 80th percentile threshold were widely reduced under historical and hist-GHG forcings, indicating increased frequency. CSDH showed the most extensive shortening (61.1% and 62.1% of land areas under historical and hist-GHG, respectively).
• Interpretable machine learning (LightGBM with SHAP) revealed temperature as the primary driver across all event types and scenarios (relative importance 0.15 to 0.30), with precipitation and vapor pressure deficit (VPD) playing event-dependent roles.
• Future projections (2015–2100) under high-emission pathways (SSP5-8.5) indicate significant severity growth (average increases of 6.8%, 9.4%, and 15.4% for CMDH, CHDH, and CSDH, respectively, by 2100) and sharply rising global population exposure (slopes of 0.22, 0.23, and 0.65 for CMDH, CHDH, and CSDH under SSP5-8.5), concentrated in tropical and temperate regions.

Contributions

• Provides a comprehensive global assessment of anthropogenic influences on three distinct types of compound drought and heatwave (CDHW) events (precipitation-based, runoff-based, and soil-moisture-based), addressing a gap in global-scale exploration of CHDH and CSDH.
• Quantifies the relative contributions of specific anthropogenic drivers (greenhouse gases versus aerosols) to CDHW changes, disentangling their differential impacts.
• Employs interpretable machine learning (LightGBM with SHAP) to identify dominant hydrometeorological drivers of CDHWs across different climate zones and forcing scenarios, offering mechanistic insights.
• Integrates historical attribution with future projections under multiple Shared Socioeconomic Pathways (SSPs) and evaluates population exposure dynamics, providing essential insights for targeted mitigation and adaptation strategies.

Funding

• Postdoctoral Fellowship Program (Grant Number BX20250027)
• China Postdoctoral Science Foundation (Grant Number BX20250027)
• National Key Research and Development Program of China (2024YFB3908600, 2023YFC3209101)
• Key Research and Development Program of Hubei Province (2023BCA003)
• National Natural Science Foundation of China (42090010, 42401554)
• Natural Science Foundation of Hubei Province of China (2024AFB061)

Citation

@article{Huang2025Differential,
  author = {Huang, Shuzhe and Wang, Siqi and Wang, Chao and Zhang, Xiang and Gong, Jianya and Chen, Nengcheng},
  title = {Differential sensitivities of three types of compound drought and heatwave events to human-induced climate change across the globe},
  journal = {Weather and Climate Extremes},
  year = {2025},
  doi = {10.1016/j.wace.2025.100836},
  url = {https://doi.org/10.1016/j.wace.2025.100836}
}

Generated by BiblioAssistant using gemini-2.5-flash (Google API)