AI预测2026年夏季中国中部将出现干旱，且可解释原因
Interpretable AI predicts a 2026 summer dry anomaly in central China
- 用深度学习将大气环流预测转化为降水预估
- 3-5月初始化预测均显示2026夏中国中部干旱
- 首次实现气候预测的物理可解释性，适合政策决策者
季节性降水异常主要受大气环流调控，动力模型对环流的预测比对降水本身更可靠。本文采用深度学习模型，将动力环流预测转换为降水估算。从3月至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。回溯评估显示，在类似历史年份中预测技能更高，这些年份普遍呈现前冬至夏持续的赤道太平洋中部增暖。该增暖有利于西太平洋—南海—华南地区异常气旋环流，导致偏北风与水汽辐散，共同抑制中国中部降水。支持这一机制的是层间相关性传播（LRP）分析，独立识别出偏北风是模型输入中主导预测的因素。扰动实验进一步验证：移除LRP识别特征后，干旱异常即消失。本框架为人工智能生成的区域气候预测提供了物理可解释性，使在观测数据可用前即可开展基于证据的评估。
原文摘要 · Abstract (English)
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 over central China. Supporting this mechanism, layer-wise relevance propagation (LRP) independently identifies these northerly winds as the dominant driver of the prediction among all model inputs. Perturbation tests supported this attribution: removing LRP-identified features effectively eliminates the dry anomaly. Our framework thus provides physically interpretable explanations for AI-derived regional climate projections, facilitating evidence-based assessment before observational data become available.
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