Abstract
Skillful seasonal climate prediction is critical for food and water security over the world's heavily populated regions, such as in continental East Asia. Current models, however, face significant difficulties in predicting the summer mean rainfall anomaly over continental East Asia, and forecasting rainfall spatiotemporal evolution presents an even greater challenge. Here, we benefit from integrating the spatiotemporal evolution of rainfall to identify the most crucial patterns intrinsic to continental East-Asian rainfall anomalies. A physical-statistical prediction model is developed to capture the predictability offered by these patterns through a detection of precursor signals that describe slowly varying lower boundary conditions. The presented model demonstrates a prediction skill of 0.51, at least twice as high as that of the best dynamical models available (0.26), indicating improved prediction for both the spatiotemporal evolution and summer mean of rainfall anomalies. This advance marks a crucial step toward delivering skillful seasonal predictions to populations in need of new tools for managing risks of both near-term climate disasters, such as floods and droughts, and long-term climate change.