Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models. In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.
极端梅雨降雨 (MYR) 会对中国的社会和经济发展造成灾难性影响. 尽管气候模型在模拟气候上已有重要进展, 但这些模型通常难以模拟局部到区域的极端降雨过程(例如, 极端梅雨降雨). 目前来说, 气候模型能较好地模拟大尺度气候模式(LSCM). 由于 极端梅雨降雨 与不同的 大尺度气候模式 之间存在密切关系, 因此, 我们有可能基于大尺度气候模式来构建因果关系引导的数据模型. 该数据模型有望应用于气候模型的模拟以改善其在模拟极端梅雨降雨中的表现. 在此探索性研究中, 我们证明了基于因果关系引导方法的数据模型可以通过已知的大尺度气候模式来建立, 并有效模拟极端梅雨降雨. 该统计模型內的预测变量的相关性以及时间分辨率的重要性都与文献一致. 该研究结果证实了因果关系引导方法在研究东亚夏季风 (EASM) 等复杂环流系统中的可靠性. 本文也讨论了当前方法的一些局限性和可能的改进方法. 因果关系引导方法的应用为未来研究复杂的东亚夏季风中的相互作用开辟了新的可能性.
Keywords: Mei-yu front; causality-guided approach; extreme rainfall; large-scale climate modes.
© The Author(s) 2022.