Machine Learning-Based Fusion of Multi-Source Daily Precipitation Products for the Tibetan Plateau Rainy Season

Abstract. As the "Asian Water Tower," the Tibetan Plateau (TP) critically influences regional water security and global climate. Yet, due to its complex terrain and scarce observations, existing precipitation products poorly represent precipitation characteristics over the western.TP. Here, we present 3DMergePrec (3DM), a 0.25° daily precipitation dataset for the TP rainy seasons (1961–2021), generated by fusing 12 mainstream products using a deep learning framework combining Graph Attention Networks and 3D Convolutional Neural Networks. Validated against long-term observations (CMA stations) and independently verified with automatic stations in the western and central-western TP, 3DM demonstrates robust performance: Overall, it reduces mean squared error by 30–40% compared to satellite-only products (e.g., TRMM, GPM) and effectively mitigates the high-error belt in the southeastern TP. Crucially, in the data-sparse western TP, 3DM achieves RMSE reductions of 25–40% (e.g., mean squared error of 10.78 mm in the Qiangtang region), outperforming existing products. Its long-term precipitation trends closely align with observations, surpassing most counterparts. Limitations include underestimation of extreme precipitation frequency and overestimation of light precipitation days, with limited improvement in precipitation detection—likely due to the lack of dynamical constraints. Overall, 3DM offers stable, spatially continuous, and accurate precipitation estimates, particularly in the western TP, providing a valuable long-term dataset to support climate change studies across the region.