[论文] Interpretable AI predicts a 2026 summer dry anomaly in central China

论文概要

研究领域: ML 作者: Anran Wang, Wen Shi, Yong Luo, Jianbin Huang, Lijuan Chen, Junhu Zhao, Weixin Jin, Huihui Yuan 发布时间: 2026-08-19 arXiv: 2608.19163

中文摘要

季节性降水异常主要由大气环流调节，动态模型对环流的预测比降水本身更可靠。在这里，我们采用一个深度学习模型，将动态环流预测转化为降水估计。从3月到5月初始化的预测一致表明2026年夏季中国中部将出现干旱异常。回顾性评估显示在类似年份中具有更高的预测技能，这些年份也往往具有从前冬持续到夏季的赤道太平洋中部变暖。这种变暖有利于西太平洋-南海-华南地区出现异常气旋环流，这引发北风和水汽辐散，共同抑制中国中部的降雨。支持这一机制，逐层相关性传播（LRP）独立地将这些北风识别为所有模型输入中预测的主导驱动因素。扰动测试支持这种归因：移除LRP识别的特征有效地消除了干旱异常。因此，我们的框架为AI衍生的区域气候预测提供了物理上可解释的解释，便于在观测数据可用之前进行基于证据的评估。

原文摘要

Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precipitation itself. Here, we employ a deep learning model that translates dynamical circulation predictions into precipitation estimates. Predictions initialized from March to May consistently indicate a dry anomaly over central China in summer 2026. Retrospective evaluations revealed higher predictive skill in the analogue years, which also tended to feature central equatorial Pacific warming persisting from the preceding winter into summer. This warming favors an anomalous cyclonic circulation over the western North Pacific-South China Sea-South China region, which induces northerly winds and moisture divergence that jointly suppress rainfall o...

--- *自动采集于 2026-08-21*

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