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<p>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 time scales. 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 period 2021–24 achieve significant spatial anomaly correlation coefficients, reaching a maximum value of 0.83. Additional verifications by station observations show that the model hits most station anomalies, achieving a mean predictive skill score of 90.</p>

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A Scale Separation Hybrid Predictive Model and Its Application to Predict Summer Monthly Precipitation in Northeast China

  • 期刊论文

  • 发表日期：

    2025年12月13日

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  • Lei Yu,
  • Aihui Wang,
  • Changzheng Liu

摘要

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 time scales. 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 period 2021–24 achieve significant spatial anomaly correlation coefficients, reaching a maximum value of 0.83. Additional verifications by station observations show that the model hits most station anomalies, achieving a mean predictive skill score of 90.