A Tale of Two Unprecedented Droughts in Southeast Asia: Physical Drivers and Impending Future Risks

Abstract Conventional wisdom suggests that tropical droughts in Southeast Asia are closely linked to natural climate variability like El Niño. However, the extreme 2014 drought occurred independently of El Niño, suggesting other dynamic forcings at play. Here we use moisture budget analysis, moisture tracking, and physics‐informed joint probability modeling to disentangle the interplay between dynamic and thermodynamic drivers behind this unprecedented drought and to assess future drought risks under climate change. We find that the 2014 drought primarily resulted from air subsidence due to anticyclone‐driven mid‐troposphere divergence, leading to significant precipitation deficits, which are further intensified by reduced marine moisture inflow from the West Pacific. Incorporating the dynamic and thermodynamic drivers into our bivariate probabilistic analysis, we find that the likelihood of 2014‐like droughts will increase by 25% and 43% under stabilized and business as usual pathways, respectively, by mid‐century (2030–2064). Such increases in drought risk are dominated by climate‐change‐induced changes in dynamic processes, particularly reduced mid‐troposphere vertical motion, where thermodynamic processes and the dependence structure between the two play a less significant role. However, significant inter‐model inconsistence in attributing the relative importance of these factors highlights the challenges of using current climate models for robust risk assessment.

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Publication Details

Journal
Earth s Future
Published
2025-08-01
DOI
https://doi.org/10.1029/2025ef005971
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

A Tale of Two Unprecedented Droughts in Southeast Asia: Physical Drivers and Impending Future Risks

Zhongwang Wei, Wenpeng Zhao, Xiao Peng, Ming Pan et al.
Earth s Future
Climate variability and models
article

A Tale of Two Unprecedented Droughts in Southeast Asia: Physical Drivers and Impending Future Risks

Zhongwang Wei, Wenpeng Zhao, Xiao Peng, Ming Pan, Xiaogang He, Zhixing Niu, Xinyue Liu, Shuping Ma
article en

Abstract

Abstract Conventional wisdom suggests that tropical droughts in Southeast Asia are closely linked to natural climate variability like El Niño. However, the extreme 2014 drought occurred independently of El Niño, suggesting other dynamic forcings at play. Here we use moisture budget analysis, moisture tracking, and physics‐informed joint probability modeling to disentangle the interplay between dynamic and thermodynamic drivers behind this unprecedented drought and to assess future drought risks under climate change. We find that the 2014 drought primarily resulted from air subsidence due to anticyclone‐driven mid‐troposphere divergence, leading to significant precipitation deficits, which are further intensified by reduced marine moisture inflow from the West Pacific. Incorporating the dynamic and thermodynamic drivers into our bivariate probabilistic analysis, we find that the likelihood of 2014‐like droughts will increase by 25% and 43% under stabilized and business as usual pathways, respectively, by mid‐century (2030–2064). Such increases in drought risk are dominated by climate‐change‐induced changes in dynamic processes, particularly reduced mid‐troposphere vertical motion, where thermodynamic processes and the dependence structure between the two play a less significant role. However, significant inter‐model inconsistence in attributing the relative importance of these factors highlights the challenges of using current climate models for robust risk assessment.

Earth s FutureVol. 13(8)
Sun Yat-sen University (CN), National University of Singapore (SG), University of California San Diego (US), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Yangzhou University (CN)
Agência Nacional de Águas, National Research Foundation, National Research Foundation Singapore
Climate action
Openalex Percentile: Top 81%
Climate variability and models
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