Skillful summer precipitation prediction in China using an attention-based Bayesian deep learning network

Summer precipitation in China, primarily driven by the East Asian summer monsoon, holds significant socioeconomic implications. Skillful prediction of summer precipitation requires effective multi-model integration of operational climate models. To improve the model integration, this study proposes a novel Bayesian deep learning (BDL) network that integrates convolutional neural networks (CNNs) with attention mechanisms. The BDL network is evaluated using four operational climate models: ECMWF_SEAS51, JMA_CPS3, NCC_CSM11, and NCEP_CFS2. Compared to conventional Bayesian Model Averaging (BMA), the BDL network more accurately captures the spatiotemporal patterns of summer precipitation, improving the anomaly correlation coefficient (ACC), the prediction score (PS), and the root-mean-square error (RMSE). These improvements are primarily attributed to the adaptive weighting of individual model over time. Further analysis identifies NCEP_CFS2 and ECMWF_SEAS51 as the primary contributors to the integrated prediction. This study presents a new perspective for model integration via deep learning, providing an effective approach to enhance summer precipitation prediction.