The summer MJO exhibits different characteristics from its winter counterpart, particularly distinguished by propagation in both eastward and northward directions, which is relatively less understood. Here, we explore the primary sources of the summer MJO predictability using Machine Learning (ML) based on the long-term climate model simulation and its transfer learning with the observational data. Our ML-based summer MJO prediction model shows a correlation skill of 0.5 at about 24-day forecast lead time. By utilizing eXplainable Artificial Intelligence (XAI), we discern Precipitable Water (PW) and Surface Temperature (TS) as the most influential sources for the summer MJO predictability. We especially identify the roles of PW and TS in the eastern and northern Indian Ocean (EIO and NIO) regions on the propagation characteristics of the summer MJO through XAI-based sensitivity experiments. These results suggest that ML-based approaches are useful for identifying sources of predictability and their roles in climate phenomena.

夏季 MJO 表现出与冬季不同的特征，特别是向东和向北传播，这相对较少被理解。在这里，我们基于长期气候模型模拟及其对观测数据的迁移学习，使用机器学习 （ML） 探索夏季 MJO 可预测性的主要来源。我们基于 ML 的夏季 MJO 预测模型显示，在大约 24 天的预测提前期内，相关技能为 0.5。通过利用可解释的人工智能 （XAI），我们发现可降水 （PW） 和表面温度 （TS） 是夏季 MJO 可预测性的最有影响力的来源。我们特别通过基于 XAI 的敏感性实验确定了东印度洋和北印度洋 （EIO 和 NIO） 地区 PW 和 TS 对夏季 MJO 传播特性的作用。这些结果表明，基于 ML 的方法可用于识别可预测性的来源及其在气候现象中的作用。