Current state‐of‐the‐art dynamical models continue to face challenges in accurately forecasting the seasonal evolution of spatiotemporal precipitation anomalies over High Mountain Asia (HMA). We introduced a framework that identifies dominant spatiotemporal patterns by jointly integrating the spatial and temporal signals of precipitation anomalies over HMA on multi‐year scales, in contrast to conventional approaches that teat spatial and temporal variations separately. Consideration of the joint patterns provides a physical constraint that helps identify those more robust predictors, which as a result, are closely linked to the full seasonal evolution of precipitation anomalies. Consequently, the complex task of forecasting the seasonal evolution of total precipitation anomalies is reduced to predicting the year‐to‐year variability of these spatiotemporal patterns. Building on these patterns, we developed a physical‐statistical prediction model that links the variability of each spatiotemporal pattern to its precursors using multiple linear regression. Compared to current dynamical multi‐model ensemble forecasts, this new model demonstrates significantly improved skills in predicting both the detailed seasonal evolution and the rainy‐season mean of precipitation anomalies over HMA. This advancement holds significant potentials for enhancing early warnings of climate hazards and improving water resource management in vulnerable high mountain regions.