Attribution of the 2022 Asian Spatially Compound Heatwave‐Flooding Event to Atmospheric Circulation, La Niña and Anthropogenic Warming

Abstract An unprecedented spatially compound heatwave‐flooding event (SCHFE) affected the Yangtze River Basin (YRB) and Northwestern South Asia (NWSA) during summer 2022, causing severe socioeconomic impacts. However, attributing such compound extremes remains challenging due to the lack of integrated frameworks that capture both physical causality and probabilistic assessment. Here, we introduce a storyline‐probability combined attribution framework to quantify individual contributions of atmospheric circulation, La Niña, and anthropogenic warming to this unprecedented 2022 SCHFE. Reanalysis and simulations show that La Niña generated large‐scale circulation patterns conducive to the SCHFE, amplifying the event's severity even when circulation anomalies were weaker than 2022. The SCHFE‐associated atmospheric circulation dynamics accounted for 49.21% (38.64%–59.41%) of the SCHFE's intensity and increased its likelihood by a factor of 4.5 compared with unrelated circulation. Anthropogenic warming contributed 42.31% (34.32%–49.85%) of the intensity throughout the thermodynamic effects, comparable to the circulation‐induced dynamic contribution, while also enhancing the occurrence probability of both La Niña and SCHFE. Projections further indicate that La Niña events will substantially elevate the probability of SCHFEs under continued warming. These findings underscore the need to incorporate atmospheric circulation, La Niña, and anthropogenic warming into early warning systems for such compound extremes.

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Publication Details

Journal
Journal of Geophysical Research Atmospheres
Published
2026-02-12
DOI
https://doi.org/10.1029/2025jd045461
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Attribution of the 2022 Asian Spatially Compound Heatwave‐Flooding Event to Atmospheric Circulation, La Niña and Anthropogenic Warming

Tianshun Gu, Liangwei Li, Xihui Gu, Jianjun Wang et al.
Journal of Geophysical Research Atmospheres
Climate variability and models
article

Attribution of the 2022 Asian Spatially Compound Heatwave‐Flooding Event to Atmospheric Circulation, La Niña and Anthropogenic Warming

Tianshun Gu, Liangwei Li, Xihui Gu, Jianjun Wang, Zaiming Jiang, Lunche Wang, Wen Xiao
article en

Abstract

Abstract An unprecedented spatially compound heatwave‐flooding event (SCHFE) affected the Yangtze River Basin (YRB) and Northwestern South Asia (NWSA) during summer 2022, causing severe socioeconomic impacts. However, attributing such compound extremes remains challenging due to the lack of integrated frameworks that capture both physical causality and probabilistic assessment. Here, we introduce a storyline‐probability combined attribution framework to quantify individual contributions of atmospheric circulation, La Niña, and anthropogenic warming to this unprecedented 2022 SCHFE. Reanalysis and simulations show that La Niña generated large‐scale circulation patterns conducive to the SCHFE, amplifying the event's severity even when circulation anomalies were weaker than 2022. The SCHFE‐associated atmospheric circulation dynamics accounted for 49.21% (38.64%–59.41%) of the SCHFE's intensity and increased its likelihood by a factor of 4.5 compared with unrelated circulation. Anthropogenic warming contributed 42.31% (34.32%–49.85%) of the intensity throughout the thermodynamic effects, comparable to the circulation‐induced dynamic contribution, while also enhancing the occurrence probability of both La Niña and SCHFE. Projections further indicate that La Niña events will substantially elevate the probability of SCHFEs under continued warming. These findings underscore the need to incorporate atmospheric circulation, La Niña, and anthropogenic warming into early warning systems for such compound extremes.

Journal of Geophysical Research AtmospheresVol. 131(4)
China University of Geosciences (CN), Shenzhen Research Institute of China University of Geosciences (CN), Hebei Meteorological Bureau (CN)
Natural Science Foundation of Shenzhen City, National Natural Science Foundation of China
Openalex Percentile: Top 69%
Climate variability and models
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