Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution
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 fo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55271-1 |
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| author | Jieru Ma Hong-Li Ren Ming Cai Yi Deng Chenguang Zhou Jian Li Huizheng Che Lin Wang |
| author_facet | Jieru Ma Hong-Li Ren Ming Cai Yi Deng Chenguang Zhou Jian Li Huizheng Che Lin Wang |
| author_sort | Jieru Ma |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5530220ce4854d439f59685d15ca2df5 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-5530220ce4854d439f59685d15ca2df52025-01-05T12:39:14ZengNature PortfolioNature Communications2041-17232025-01-0116111010.1038/s41467-024-55271-1Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolutionJieru Ma0Hong-Li Ren1Ming Cai2Yi Deng3Chenguang Zhou4Jian Li5Huizheng Che6Lin Wang7State Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological SciencesState Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological SciencesDepartment of Earth, Ocean, and Atmospheric Science, Florida State UniversitySchool of Earth and Atmospheric Sciences, Georgia Institute of TechnologyState Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological SciencesState Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological SciencesKey Laboratory of Atmospheric Chemistry of China Meteorological Administration, Chinese Academy of Meteorological SciencesInstitute of Artificial Intelligence for Meteorology, Chinese Academy of Meteorological SciencesAbstract 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.https://doi.org/10.1038/s41467-024-55271-1 |
| spellingShingle | Jieru Ma Hong-Li Ren Ming Cai Yi Deng Chenguang Zhou Jian Li Huizheng Che Lin Wang Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution Nature Communications |
| title | Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution |
| title_full | Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution |
| title_fullStr | Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution |
| title_full_unstemmed | Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution |
| title_short | Skillful seasonal predictions of continental East-Asian summer rainfall by integrating its spatio-temporal evolution |
| title_sort | skillful seasonal predictions of continental east asian summer rainfall by integrating its spatio temporal evolution |
| url | https://doi.org/10.1038/s41467-024-55271-1 |
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