A scale separation hybrid predictive model and its application to predict summer monthly precipitation in Northeast China

Northeast China serves as an important crop production region. Accurately forecasting summer precipitation in Northeast China (NEC-PR) has been a challenge due to its wide range of time scales influenced by varying climatic conditions. This study presents a scale separation hybrid statistical model with recurrent neural network (SS-RNN) to predict the summer monthly NEC-PR. The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales. This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various timescales. Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models, particularly in predicting the spatial pattern of the NEC-PR. In this paper we take August, the month of the highest NEC-PR, to assess our model skill. Independent forecasts of the August NEC-PR over the years 2021-2024 achieved significant spatial anomaly correlation coefficients, reaching a maximum value of 0.83. Additional verifications by station observations show that the model hit most station anomalies, achieving a mean predictive skill score of 90.