Climate change affects human health; however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here, we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-induced warming, during the period 1991–2018. Across all study countries, we find that 37.0% (range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change and that increased mortality is evident on every continent. Burdens varied geographically but were of the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change.
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We thank the participants of the ISIMIP Health workshop in Barcelona in November 2018 where this work was discussed for the first time. This study was supported by the Medical Research Council UK (grant no. MR/M022625/1), the Natural Environment Research Council UK (grant no. NE/R009384/1) and the European Union’s Horizon 2020 Project Exhaustion (grant no. 820655). N.S. was supported by the NIEHS-funded HERCULES Center (P30ES019776). Y.H. was supported by the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, Japan (JPMEERF15S11412). J.J.J.K.J. was supported by Academy of Finland (grant no. 310372). V.H. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (grant no. PCIN-2017-046) and the German Federal Ministry of Education and Research (grant no. 01LS1201A2). J.K. and A.U. were supported by the Czech Science Foundation (grant no. 20-28560S). J.M. was supported by the Fundação para a Ciência e a Tecnologia (FCT) (SFRH/BPD/115112/2016). S.R. and F.d.R. were supported by European Union’s Horizon 2020 Project EXHAUSTION (grant no. 820655). M.H. was supported by the Japan Science and Technology Agency as part of SICORP, grant no. JPMJSC20E4. Y.G. was supported by the Career Development Fellowship of the Australian National Health and Medical Research Council (APP1163693). S.L. was support by the Early Career Fellowship of the Australian National Health and Medical Research Council (APP1109193). Y.L.L.G. was supported by the Taiwan Ministry of Science and Technology (MOST110-2918-I-002-007) as a visiting academic at the University of Sydney.
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Katrin Burkart, Daniel Mitchell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended Data Fig. 1 Time series plots of the warm-season mean daily temperatures in each scenario provided by each model.
The time series plots depict the temporal trends in average warm-season temperatures across the 732 locations included in the study. Factual scenario (with natural and anthropogenic forcings) is depicted in brown, while counterfactual scenario (with natural forcings only) in orange. The grey dark area corresponds to the study period 1991–2018. (ACC: ACCESS-ESM1-5, CAN: CanESM5, CNR: CESM2, FGO: FGOALS-g3, GFD: GFDL-ESM4, HAD: HadGEM3-GC31-LL, IPS: IPSL-CM6A-LR, MIR: MIROC6, MRI: MRI-ESM2-0, Nor: NorESM2-LM).
Extended Data Fig. 2 Time series plots of the warm-season mean daily temperatures in each scenario in the 43 countries included in the study.
As Fig.1, factual scenario (with natural and anthropogenic forcings) is depicted in brown, while counterfactual scenario (with natural forcings only) in orange. The shaded area corresponds to 1 standard deviation across model-specific average estimates. The dashed line shows the start of the study period (1991–2018).
Extended Data Fig. 3 Country-averaged warm-season temperature distributions modelled in each scenario.
As Fig.1, factual scenario (with natural and anthropogenic forcings) is depicted in brown, while counterfactual scenario (with natural forcings only) in orange.
Extended Data Fig. 4 Location-specific heat-related mortality attributed to human-induced climate change (CC) between 1991–2018.
Map with the location-specific estimates of heat-related mortality fractions attributed to human-induced climate change (expressed in %). Estimates ranged between 0.2% and 0.8%, corresponding to the interquartile range, with a maximum value of 3.8%, and 23 locations reported an estimate below 0 (minimum value of -0.1%).
Extended Data Fig. 5 Proportion of heat-related mortality attributed to human-induced climate change (CC), between 1991–2018.
Map with the location-specific estimates of the proportion of heat-related mortality attributed to human-induced climate change (expressed in %). Estimates ranged between 28.6% and 54.2%, corresponding to the interquartile range, with a maximum value of 92%, and 1 location with estimates below 0 (minimum value of -0.1%).
Extended Data Fig. 6 Model-specific estimates of the heat-related mortality attributed to human-induced climate change (CC) for each country, expressed as mortality fraction (%).
The plot shows the model-specific estimates of heat-related mortality fraction attributed to human-induced climate change for each country (1991–2018). ACC: ACCESS-ESM1-5, CAN: CanESM5, CNR: CESM2, FGO: FGOALS-g3, GFD: GFDL-ESM4, HAD: HadGEM3-GC31-LL, IPS: IPSL-CM6A-LR, MIR: MIROC6, MRI: MRI-ESM2-0, Nor: NorESM2-LM.
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Vicedo-Cabrera, A.M., Scovronick, N., Sera, F. et al. The burden of heat-related mortality attributable to recent human-induced climate change. Nat. Clim. Chang. 11, 492–500 (2021). https://doi.org/10.1038/s41558-021-01058-x
Nature Climate Change (2021)