Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution

Jieru Ma, Hong-Li Ren (), Ming Cai, Yi Deng, Chenguang Zhou, Jian Li, Huizheng Che and Lin Wang
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Jieru Ma: Chinese Academy of Meteorological Sciences
Hong-Li Ren: Chinese Academy of Meteorological Sciences
Ming Cai: Florida State University
Yi Deng: Georgia Institute of Technology
Chenguang Zhou: Chinese Academy of Meteorological Sciences
Jian Li: Chinese Academy of Meteorological Sciences
Huizheng Che: Chinese Academy of Meteorological Sciences
Lin Wang: Chinese Academy of Meteorological Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Skillful seasonal climate prediction is critical for food and water security over the world’s heavily populated regions, such as in continental East Asia. Current models, however, face significant difficulties in predicting the summer mean rainfall anomaly over continental East Asia, and forecasting rainfall spatiotemporal evolution presents an even greater challenge. Here, we benefit from integrating the spatiotemporal evolution of rainfall to identify the most crucial patterns intrinsic to continental East-Asian rainfall anomalies. A physical-statistical prediction model is developed to capture the predictability offered by these patterns through a detection of precursor signals that describe slowly varying lower boundary conditions. The presented model demonstrates a prediction skill of 0.51, at least twice as high as that of the best dynamical models available (0.26), indicating improved prediction for both the spatiotemporal evolution and summer mean of rainfall anomalies. This advance marks a crucial step toward delivering skillful seasonal predictions to populations in need of new tools for managing risks of both near-term climate disasters, such as floods and droughts, and long-term climate change.

Date: 2025
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DOI: 10.1038/s41467-024-55271-1

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