Adeyeri et al. (2025) Asymmetric heatwave intensification under divergent climate change mitigation pathways amplifies urban–rural exposure disparities

Identification

• Journal: Weather and Climate Extremes
• Year: 2025
• Date: 2025-10-29
• Authors: Oluwafemi E. Adeyeri, Wen Zhou, Christopher E. Ndehedehe, Kazeem A. Ishola, Akintomide A. Akinsanola, Naveed Ahmed, Xuan Wang
• DOI: 10.1016/j.wace.2025.100821

Research Groups

• ARC Centre of Excellence for the Weather of the 21st Century, Fenner School of Environment and Society, The Australian National University, Canberra, Australia
• Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate, Ministry of Education and Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China
• Key Laboratory for Polar Science of the MNR, Polar Research Institute of China, Shanghai, China
• School of Environment and Science, Griffith University, Nathan, QLD, Australia
• Australian Rivers Institute, Griffith University, Nathan, QLD, Australia
• Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Ireland
• National Centre for Geocomputation, Maynooth University, Maynooth, Ireland
• Department of Earth and Environmental Sciences, University of Illinois Chicago, Chicago, IL, USA
• Key Lab of Wetland and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
• Low-Carbon and Climate Impact Research Centre, School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong, China

Short Summary

This study projects future heatwave characteristics and population exposure under different climate change mitigation pathways (SSP370, SSP585) using bias-corrected climate models, revealing asymmetric heatwave intensification and significant, often comparable, exposure disparities between urban and rural populations globally.

Objective

• To quantify the spatial and temporal variability of global and regional heatwave characteristics, including frequency, intensity, magnitude, number, duration, and timing.
• To identify the impact of limiting global warming to SSP370 and SSP585 emission levels on heatwave characteristics.
• To establish the relationship between heatwave characteristics and different climate variability modes.
• To quantify exposure of rural and urban populations to heatwaves, considering changes in exposure and their attribution to climate, population dynamics, and their interactions at various scales.

Study Configuration

• Spatial Scale: Global, focusing on 50 distinct IPCC AR6 regions. All datasets regridded to a uniform 2° × 2° grid.
• Temporal Scale:
  • Historical period: 1979–2014 (35 years)
  • Mid-21st-century (Near Future): 2025–2060
  • Late-21st-century (Far Future): 2065–2100
  • Daily temporal resolution for climate data. Population data available at 10-year intervals.
  • Analysis focused on extended summer seasons: May to September (Northern Hemisphere) and November to March (Southern Hemisphere).

Methodology and Data

• Models used:
  • Ten multivariate bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) models and their ensemble mean (CMCC-ESM2, CNRM-CM6-1, INM-CM5-0, IPSL-CM6A-LR, KACE-1-0-G, MIROC6, MPI-ESM-1-2HR, MPI-ESM-1-LR, MRI-ESM2-0, UKESM1-0-LL).
  • Multivariate bias-correction method: N-dimensional probability density function transform (MBCN).
  • Shared Socioeconomic Pathways (SSPs): SSP370 (regional rivalry, 7.0 W/m²) and SSP585 (fossil-fuel development, 8.5 W/m²).
  • Heatwave definition: Excess Heat Factor (EHF), identifying periods of three or more consecutive days with positive EHF.
• Data sources:
  • Reference climate dataset: W5E5 (incorporating WATCH Forcing Data (WFDE5) for land and ERA5 for oceans), global, 0.5° × 0.5° spatial resolution, daily temporal resolution (1979–2019). Variables include daily near-surface relative humidity, specific humidity, precipitation, surface air pressure, sea level pressure, surface downwelling longwave radiation, surface downwelling shortwave radiation, near-surface wind speed, daily maximum/minimum/mean near-surface air temperature.
  • Historical rural and urban population data: Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) protocol, ISIMIP2b (approx. 0.083° spatial resolution).
  • Future rural and urban population projections: NASA Socioeconomic Data and Applications Center (0.125° resolution), leveraging a gravity-based model.
  • Climate variability mode datasets: North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Pacific-North American teleconnection pattern (PNA), Ni˜no 3.4 SST Index (NINO), Dipole Mode Index (DMI), and Tropical Northern Atlantic index (TNA) from NOAA Climate Prediction Center (CPC) archives.

Main Results

• Bias correction significantly reduces root mean square error (RMSE) in CMIP6 models for heatwave frequency (HWF), with uncorrected models overestimating HWF by up to 9 days in tropical regions.
• Heatwave characteristics are projected to intensify and onset earlier across global regions under both SSP370 and SSP585, with SSP585 leading to substantially more persistent and intense heatwaves.
• Projected increases in heatwave number (HWN) are highest in Southern Australia (SAU) under SSP585 late-21st-century, reaching 9–10 events.
• Projected increases in heatwave frequency (HWF) are highest in Northern South America (NSA) and North-Western Southern America (NWS) under SSP370 mid-21st-century, ranging from 140 to 160 days.
• Heatwave duration (HWD) is projected to increase significantly, up to 160 days in the late-21st-century under SSP370, particularly in NSA.
• Most regions show significant negative trends in heatwave timing (HWT), indicating earlier onsets of heatwaves.
• The relationship between HWF and HWN varies regionally; under future scenarios, high HWF is generally linked with high HWN in most tropical regions, while polar regions exhibit the opposite.
• Heatwave intensity (HWA, HWM) is primarily governed by radiative and advective forcing, while persistence (HWD, HWF) depends on large-scale flow stability.
• Climate variability modes exhibit highly region-specific correlations with heatwave characteristics; for instance, the Tropical North Atlantic (TNA) mode shows strong positive correlations (r ≤ 0.7) with Eastern Siberia (ESB) heatwaves.
• Rural populations often experience comparable or even higher heatwave exposure than urban populations, challenging the assumption that urban areas universally face greater heat-related impacts.
• Under SSP370, the Tibetan region is projected to experience rural population exposure to HWF totaling 15 million person-days, compared to 5 million person-days in urban population exposure.
• In East Asia (EAS) under SSP370, the climate effect dominates rural population exposure changes (90%), while urban exposure varies by region.
• Under SSP585 late-21st-century, the interaction effect contributes significantly to urban population exposure in regions like South-Eastern Africa (SEAF) and West Africa (WAF) (~50%, translating to at least 80 million person-days).

Contributions

• Quantifies the asymmetric intensification of heatwaves under divergent climate change mitigation pathways (SSP370 vs. SSP585) and their differential impacts.
• Highlights and quantifies the amplification of urban-rural heatwave exposure disparities, challenging the common assumption that urban areas universally experience greater heat-related impacts due to the urban heat island effect.
• Employs multivariate bias-corrected CMIP6 models, significantly improving the accuracy and reliability of climate projections for impact studies.
• Utilizes the Excess Heat Factor (EHF) for heatwave definition, providing a comprehensive metric that integrates both climatological significance and physiological acclimatization aspects of heat stress.
• Provides a detailed analysis of six distinct heatwave characteristics (frequency, amplitude, magnitude, number, duration, timing) and their complex relationships with major climate variability modes at global and regional scales.
• Decomposes population exposure changes into climate, population, and interaction effects, offering a nuanced understanding of the drivers behind future heatwave exposure.
• Reveals a lengthening heatwave season with significantly earlier onsets across many global regions, indicating an extended period of potential heat stress.

Funding

• Australian Research Council Grant CE230100012 (Oluwafemi E. Adeyeri)
• Australian Research Council Grant DE230101327 (Christopher E. Ndehedehe)
• National Natural Science Foundation of China Grants 42288101, 42120104001 (Wen Zhou)

Citation

@article{Adeyeri2025Asymmetric,
  author = {Adeyeri, Oluwafemi E. and Zhou, Wen and Ndehedehe, Christopher E. and Ishola, Kazeem A. and Akinsanola, Akintomide A. and Ahmed, Naveed and Wang, Xuan},
  title = {Asymmetric heatwave intensification under divergent climate change mitigation pathways amplifies urban–rural exposure disparities},
  journal = {Weather and Climate Extremes},
  year = {2025},
  doi = {10.1016/j.wace.2025.100821},
  url = {https://doi.org/10.1016/j.wace.2025.100821}
}