Feng et al. (2025) Compound drought-heatwaves in China: driving factors and risks

Identification

• Journal: Natural Hazards
• Year: 2025
• Date: 2025-09-11
• Authors: Anlan Feng, Qiang Zhang, Xihui Gu, Vijay P. Singh, Lei Hu, Yixin Sun, Jiaqi Zhao
• DOI: 10.1007/s11069-025-07621-5

Research Groups

• Faculty of Geographical Science, Beijing Normal University, Beijing, China
• Advanced Interdisciplinary Institute of Environment and Ecology, Beijing Normal University, Zhuhai, China
• School of Environmental Studies, China University of Geosciences, Wuhan, China
• Department of Biological and Agricultural Engineering and Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA
• National Water Center, UAE University, Al Ain, UAE

Short Summary

This study mapped the spatiotemporal patterns, identified driving factors, and assessed the risks of compound drought-heatwaves (CDHs) across China from 1961 to 2020, revealing an overall increasing trend, particularly after 1990, driven mainly by temperature changes.

Objective

• To map the spatiotemporal characteristics of compound drought-heatwaves (CDHs) in China from 1961 to 2020.
• To unravel the primary driving factors behind CDHs at both regional and pixel scales.
• To develop and apply a comprehensive risk assessment system for CDHs across China, based on hazard, exposure, and vulnerability.

Study Configuration

• Spatial Scale: Mainland China, sub-regionalized into eight climate zones (Western arid zone, Qinghai-Tibet Plateau, Eastern arid zone, Southwest China, Northeast China, Northern China, Central China, and Southern China). Data resolutions include 0.25° × 0.25° for climate variables, 0.5° × 0.5° for SPEI, and 1 km for NDVI.
• Temporal Scale: 1961–2020 for climate data and CDH analysis; 1995–2020 for statistical data and risk assessment. Daily climate data and monthly SPEI were used.

Methodology and Data

• Models used:
  • CDH Identification: Heatwave defined as at least three consecutive days with daily maximum temperature (Tmax) greater than the historical 90th percentile threshold. Drought defined as Standardized Precipitation Evapotranspiration Index (SPEI) < -1. CDHs defined as heatwaves occurring during drought months.
  • Correlation Analysis: Pearson correlation coefficient to assess relationships between CDHs and climate factors.
  • Risk Assessment: Integrated hazard-exposure-vulnerability framework based on the IPCC Sixth Assessment Report. Analytic Hierarchy Process (AHP) was used to assign weights to 12 selected indicators.
• Data sources:
  • Climate Data: CN05.1 daily maximum temperature (Tmax), daily average temperature (Tm), sunshine duration (Ssd), daily minimum temperature (Tmin), precipitation (Pre), relative humidity (Rhu), and average wind speed (Win) from 1961 to 2020, at 0.25° × 0.25° spatial resolution, derived from approximately 2,400 in-situ stations across China.
  • Drought Index: Monthly SPEI index data from the global SPEI database (https://spei.csic.es/) at 0.5° × 0.5° spatial resolution.
  • Vegetation Index: Normalized Difference Vegetation Index (NDVI) data for 2000–2020 (1 km resolution) from the Earth Resources Data Cloud platform, and 1995 NDVI data (8 km resolution) from China's annual vegetation index dataset.
  • Socio-economic Data: Statistical data of Chinese provinces from 1995 to 2020, sourced from the China Statistical Yearbook and annual provincial statistical yearbooks.

Main Results

• From 1961 to 2020, the frequency and duration of CDHs in China showed an overall upward trend, with a decreasing trend before 1990 and a marked increasing trend after 1990. Spatially, high CDH occurrence was identified in the Western arid zone (WAZ), Eastern arid zone (EAZ), southern Southwest China (SWC), southern Qinghai-Tibet Plateau (QTP), and Central China (CC).
• At both regional and pixel scales, CDHs were most sensitive to temperature changes. Daily maximum temperature, mean temperature, and sunshine duration were significantly positively correlated with CDHs, while precipitation and relative humidity were significantly negatively correlated. A decrease in average wind speed potentially favored CDH occurrence, possibly reflecting enhanced urban heat island effects.
• CDH hazard showed an inverted U-shaped spatial distribution, with slightly high and high hazards concentrated in WAZ, EAZ, North China (NC), and CC. CDH exposure exhibited a "high in the east and low in the west" pattern, indicating greater potential losses in SWC, Northeast China (NEC), NC, CC, and Southern China (SC). CDH vulnerability was mainly concentrated in NEC, NC, CC, and parts of SWC, showing an increasing trend across China, particularly in NEC, NC, and CC.
• High CDH risk regions were identified in NEC and NC. By 2020, some areas in NEC and NC reached high-risk levels, collectively accounting for 11% of mainland China. WAZ and QTP had low risk due to lower population and socio-economic development, but their fragile ecosystems remain vulnerable.

Contributions

• Developed a comprehensive and dynamic risk evaluation system for CDHs in China, integrating hazard, exposure, and vulnerability dimensions, addressing previous assessment shortcomings.
• Provided a detailed spatiotemporal characterization of CDHs and their driving factors across eight distinct climatic subregions of China over a 60-year period (1961–2020).
• Identified the specific meteorological drivers of CDHs at both regional and pixel scales, highlighting the dominant role of temperature-related factors and the potential influence of wind speed on urban heat island effects.
• Offered observational evidence and scientific understanding crucial for developing targeted regional disaster prevention and control measures to mitigate CDH impacts in a warming climate.

Funding

• Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region (Grant No.: 2024A03006-2).

Citation

@article{Feng2025Compound,
  author = {Feng, Anlan and Zhang, Qiang and Gu, Xihui and Singh, Vijay P. and Hu, Lei and Sun, Yixin and Zhao, Jiaqi},
  title = {Compound drought-heatwaves in China: driving factors and risks},
  journal = {Natural Hazards},
  year = {2025},
  doi = {10.1007/s11069-025-07621-5},
  url = {https://doi.org/10.1007/s11069-025-07621-5}
}