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Rising cause-specific mortality risk and burden of compound heatwaves amid climate change

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Abstract

Global warming shifts daytime-only heatwaves to nighttime-only and day–night compound heatwaves. However, evidence on the cause-specific burdens of these heatwaves in a changing climate and ageing population is limited. Here, by analysing 1,088,742 non-accidental deaths from 272 Chinese cities, we found that compound heatwaves posed significantly higher cardiopulmonary mortality risks and burdens than daytime-only and nighttime-only heatwaves, particularly for ischaemic stroke, chronic obstructive pulmonary disease and regions with high summer temperature variation. Projections suggested substantial increases in compound heatwave-related mortality (4.0–7.6-fold) by the 2090s relative to the 2010s under medium and high greenhouse gas emission scenarios, outpacing nighttime-only heatwaves (0.7–1.9-fold) and contrasting with decreasing daytime heatwave-related mortality (0.3–0.8-fold). A strict emission control scenario (Shared Socioeconomic Pathway 1-1.9) may reverse most heatwave-related mortality increases. The confluence of global warming and ageing amplifies heatwave-related burdens, outstripping the sum of their individual impacts. Our findings underscore the importance of addressing compound heatwaves amid global warming.

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Fig. 1: Lag structures for the effects of DHW, NHW and CHW on daily cause-specific mortality in 272 main Chinese cities during the summer of 2013–2015.
Fig. 2: The cumulative RRs and AFs of cause-specific mortality associated with DHW, NHW and CHW over lag 0–6 days in 272 Chinese cities.
Fig. 3: National cumulative RRs of non-accidental mortality associated with DHW, NHW and CHW, stratified by age, gender, education level, urbanization rate and summer temperature variation.
Fig. 4: Projected changes in excess deaths associated with DHW, NHW and CHW compared with the historical period (2010–2019), classified by region.
Fig. 5: Percentage changes in excess deaths related to DHW, NHW and CHW driven individually and in combination by future climate, population size and population ageing.

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Data availability

All the data that support the findings are publicly available. ERA5–Land data were sourced from https://doi.org/10.24381/CDS.E2161BAC (ref. 43). Daily relative humidity was obtained from http://data.cma.cn/en (ref. 44). Daily air pollutants were collected from https://air.cnemc.cn:18007/ (ref. 45). Future population demographics projections are accessible via Figshare at https://doi.org/10.6084/m9.figshare.c.4605713.v1 (ref. 33). CMIP6 model outputs can be retrieved from https://esgf-node.llnl.gov/projects/cmip6/ (ref. 46). City characteristics data were sourced from https://www.stats.gov.cn/sj/ndsj/ (ref. 47). The mortality data can be applied for through a government data sharing portal at https://www.phsciencedata.cn/Share/en/index.jsp (ref. 48).

Code availability

A sample of the code to reproduce the analysis is available via GitHub at https://github.com/Simon-JD-Liu/Fudan_272cities_compound_heatwave (ref. 49).

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Acknowledgements

We thank the entire staff at the 31 provincial Centers for Disease Control and Prevention and all other local Centers for Disease Control and Prevention in Disease Surveillance Point Systems for assisting with data collection, cleaning and management. No one received financial compensation for this contribution. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which coordinated and promoted CMIP6, and we thank the climate modelling groups for producing and making available their model outputs. H.K. was supported by the National Natural Science Foundation of China (82430105). R.C. and H.K. were supported jointly by the Shanghai Municipal Science and Technology Major Project (2023SHZDZX02), Shanghai B&R Joint Laboratory Project (22230750300) and Shanghai International Science and Technology Partnership Project (21230780200).

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Author notes

  1. These authors contributed equally: Jiangdong Liu, Jinlei Qi.

  2. These authors jointly supervised this work: Renjie Chen, Maigeng Zhou.

Authors and Affiliations

  1. Department of Environmental Health, School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Lab of Health Technology Assessment, IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China

    Jiangdong Liu, Cheng He, Ya Gao, Lu Zhou, Yixiang Zhu, Haidong Kan & Renjie Chen

  2. National Center for Chronic Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China

    Jinlei Qi, Peng Yin, Wei Liu & Maigeng Zhou

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Contributions

R.C. and M.Z. contributed to the study conceptualization. J.L. contributed to the study methods. J.L. and J.Q. performed the formal analysis, wrote the original draft and contributed to the visualization of all the figures and tables. P.Y., W.L., C.H., Y.G., L.Z., Y.Z., H.K., R.C. and M.Z. contributed to the review and editing of subsequent drafts. H.K. is the senior author. R.C. and M.Z. supervised all the data analysis and paper writing. The corresponding authors (R.C. and M.Z.) accessed and verified all the data in the study and had final responsibility for the decision to submit for publication after obtaining approval from all co-authors.

Corresponding authors

Correspondence to Renjie Chen or Maigeng Zhou.

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Nature Climate Change thanks Yang Chen, Cunrui Huang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The geographic distribution and region divisions of the 272 main Chinese cities.

The black dots represent the geographic locations of individual cities.

Extended Data Fig. 2 Modeled and recalibrated summer average daily maximum and minimum temperatures in 272 main Chinese cities from 1986 to 2100.

a, Summer average daily maximum temperatures from 1986 to 2100. b, Summer average daily minimum temperatures from 1986 to 2100. Dots correspond to the average values calculated using eight general circulation models (GCMs) under two climate change scenarios (SSP2–4.5 and SSP5–8.5) and four GCMs under the SSP1–1.9 scenario. The shaded areas indicate the interquartile range (IQR) for the averages of the ensemble of GCMs for each year. SSP, shared socio-economic pathway.

Extended Data Fig. 3 Estimated annual average population structure in mainland China from 2010 to 2100 under three SSP scenarios (SSP1, SSP2, and SSP5).

a, Annual average total population size. b, Annual average population of young (5–64 years) and elderly (≥65 years). c, Annual average population of females and males. SSP, shared socio-economic pathway.

Supplementary information

Supplementary Information

Supplementary Methods 1–5, Figs. 1–17 and Tables 1–4.

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Liu, J., Qi, J., Yin, P. et al. Rising cause-specific mortality risk and burden of compound heatwaves amid climate change. Nat. Clim. Chang. 14, 1201–1209 (2024). https://doi.org/10.1038/s41558-024-02137-5

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