这是科研团队用可解释AI做气候预测研究，用模型分析大气环流与降水联系，预测干旱效果比常规方法更好。
采用的可解释式AI模型将大气环流预测转化为降水估计，预测2026年夏季中国中部会出现干旱异常。预测从3月到5月初始化后，一致表明该地区2026年夏季存在干旱情况。回顾性评估显示该模型的预测能力更强，且这类年份常伴随中太平洋升温现象。
Interpretable AI predicts a 2026 summer dry anomaly in central China
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.