Shifts in MJO behavior enhance predictability of subseasonal precipitation whiplashes

  • Tat Fan Cheng
  • , Bin Wang
  • , Fei Liu
  • , Guosen Chen
  • , Mengqian Lu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Subseasonal precipitation whiplashes, marked by sudden shifts between dry and wet extremes, can disrupt ecosystems and human well-being. Predicting these events two to six weeks in advance is crucial for disaster management. Here, we show that the propagation diversity of the Madden-Julian Oscillation (MJO)—a key source of subseasonal predictability—will alter under anthropogenic warming. This is evidenced by a 40% increase in fast-propagating events by the late 21st century. Fast-propagating MJOs may rise in a period as early as 2028–2063, increasing the global risk of precipitation whiplashes through teleconnections. We propose a heuristic framework diagnosing that MJO’s acceleration is primarily driven by enhanced atmospheric stabilization and El Niño-like sea surface warming. The expected rise in fast-propagating MJOs could improve the predictability of subseasonal weather whiplashes, offering critical lead time for disaster preparedness. Understanding these impending shifts is essential for enhancing subseasonal prediction capabilities.

Original languageEnglish
Article number3978
JournalNature Communications
Volume16
Issue number1
DOIs
StatePublished - Dec 2025
Externally publishedYes

Funding

This work was supported by National Key Research and Development Program of China grant (2024YFF0809200 to F.L.), the National Science Foundation/Climate Dynamics Award (#2025027 to B.W.), the Hong Kong Research Grant Council (RGC) Postdoctoral Fellowship Scheme 2023/24 (PDFS2324-6S05 to T.F.C), General Research Fund (16300424 to M.L.), Collaborative Research Fund (C6032-21G to M.L.), and the Theme-based Research Scheme (T22-501/23-R to M.L.). T.F.C. and M.L acknowledge the support from the Otto Poon Centre for Climate Resilience and Sustainability at HKUST. The authors acknowledge the Working Group on Coupled Modelling (WGCM) under the World Climate Research Programme (WCRP) and the climate modeling groups for making the CMIP6 model outputs available. This work was supported by National Key Research and Development Program of China grant (2024YFF0809200 to F.L.), the National Science Foundation/Climate Dynamics Award (#2025027 to B.W.), the Hong Kong Research Grant Council (RGC) Postdoctoral Fellowship Scheme 2023/24 (PDFS2324-6S05 to T.F.C), General Research Fund (16300424 to M.L.), Collaborative Research Fund (C6032-21G to M.L.), and the Theme-based Research Scheme (T22-501/23-R to M.L.). T.F.C. and M.L acknowledge the support from the Otto Poon Centre for Climate Resilience and Sustainability at HKUST. The authors acknowledge the Working Group on Coupled Modelling (WGCM) under the World Climate Research Programme (WCRP) and the climate modeling groups for making the CMIP6 model outputs available.

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