可解释AI预测2026年华中夏季干旱异常

中文摘要 AI 辅助

季节降水异常在很大程度上受大气环流调控，动力模式对大气环流的预测比降水本身更可靠。本研究采用深度学习模型将动力环流预测转化为降水估算，基于3月至5月初始化的预测一致显示，2026年华中夏季将出现干旱异常。回顾性评估表明，模拟年份的预测技巧更高，这些年份往往伴随赤道中太平洋从冬季持续至夏季的增暖，该增暖会在西北太平洋-南海-华南区域引发异常气旋性环流，进而诱导北风和水汽辐散，共同抑制华中地区降水。分层相关传播（LRP）独立识别出这些北风是模型所有输入中预测的主导驱动因子，扰动测试也支持该归因：移除LRP识别的特征可有效消除干旱异常。因此，本框架为AI生成的区域气候预测提供了物理解释，便于在观测数据获取前开展循证评估。

英文摘要

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 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.

发表机构

• Tsinghua University（清华大学）
• University of Chinese Academy of Sciences（中国科学院大学）
• National Climate Centre, China Meteorological Administration（中国气象局国家气候中心）
• Microsoft（微软）

机构由 AI 辅助整理，请以论文原文为准。