The influence of human activities on extreme heat exposure events in eastern China

doi:10.12006/j.issn.1673-1719.2025.012

Abstract

Abstract:

Heat exposure is a key indicator for assessing the risks of extreme weather and climate events. While substantial research has focused on the attribution of extreme heat events, studies on the attribution of heat exposure remain limited. By using the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and global gridded population (GlobPoP), this paper proposes an attribution method for heat exposure, exemplified by the record-breaking 2022 extreme heat event in China. Using extreme heat and heat exposure to daily maximum temperatures exceeding 35℃ and 40℃ (Tx35E and Tx40E) as indicators, it’s found that both the number of extreme heat days and heat exposure have increased during 1990-2022, with the most pronounced growth in eastern China. Based on bias-corrected model data, human activities increase the probability of early summer Tx35E events in 2022 by 1.4 times in the south of the Yangtze River and 2.4 times in the north of the Yangtze River. Human activities also lead to additional extreme heat exposure and exposure ratio. In summer, with more extreme heat days, the influence of human activities on heat exposure and exposure ratio becomes more evident. Specifically, human activities lead to extra exposure of 46.5 million and 17.9 million person∙d (3.2% and 1.2% of the total population) to temperatures above 35℃ in the south and north of the Yangtze River, respectively. Although extreme heat events above 40℃ are relatively rare, human activities still increase the likelihood of Tx40E events and lead to additional heat exposure.

Key words: CMIP6, Extreme heat, Heat exposure, Extreme event attribution

LUO Hao-Yue, SUN Ying, ZHANG Yu-Xia. The influence of human activities on extreme heat exposure events in eastern China[J]. Climate Change Research, 2025, 21(5): 671-683.

Figures/Tables 9

Table 1 The CMIP6 models used in this study

Table 2 Extreme heat and heat exposure indicators

Fig. 1 Spatial distribution of linear trends for Tx35D (a), Tx40D (b), Tx35E (c), and Tx40E (d) in China from May to August during 1990-2022

Fig. 2 Changes of population (a), Tx35D (b), Tx35E (c), Tx40D (d), and Tx40E (e) in eastern China from May to August during 1990-2022 (relative to 1995-2014)

Fig. 3 Observed 2022 anomalies of Tx35E (a) and Tx40E (b) for early summer and summer in the south and north of the Yangtze River (relative to 1995-2014)

Fig. 4 Original time series of Tx35D (a, b, e, f) and Tx40D (c, d, g, h) for early summer and summer in eastern China from 1990 to 2022, before and after bias correction

Fig. 5 Probability density distributions of Tx35E (a, b, e, f) and Tx40E (c, d, g, h) for early summer and summer in the south and north of the Yangtze River, under ALL and NAT forcing (relative to 1995-2014)

Fig. 6 Risk ratio, probability, and their 95% uncertainty intervals for extreme heat exposure events Tx35E (a, c) and Tx40E (b, d) in early summer and summer of 2022 in the south and north of the Yangtze River

Fig. 7 Heat exposure and exposure ratio under different forcings for Tx35E (a, b, e, f) and Tx40E (c, d, g, h). (CMIP6 multi-model ensemble mean, vertical lines represent the 5%?95% model percentile range)

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