This paper proposes a combination model based on CEEMDAN-SE-ARIMA that aims to address the shortcomings of traditional time series models that cannot effectively predict modal aliased data. The proposed model combines the advantages of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the high short-term prediction accuracy of an auto-regressive integrated moving average model (ARIMA), and the fast efficiency of sample entropy (SE) reconstruction. The model is empirically analyzed for summer precipitation in Northeast China from 2016 to 2020. First, based on the fully adaptive ensemble empirical mode decomposition method, the precipitation time series is decomposed into multiple eigenmode components, and the component sequence is reconstructed according to the calculation results of the entropy of different component samples. Then, for each reconstruction component, an autoregressive moving average forecast model is constructed. Finally, the predicted value of each component is superimposed to obtain the predicted value of the combined model. Additionally, the ARIMA single model and other combined modelsare constructed to be compared with the CEEMDAN-SE-ARIMA combined model. The results show that the CEEMDAN-SE-ARIMA combined accounts for the time series’ modal aliasing characteristics, effectively improves the forecasting ability of the summer precipitation time series model in Northeast China, and has good forecast application value. Compared with the single model and other combined models, the forecast results are improved. MASE decreases by 0.02—0.91 mm, RMSE decreases by 0.80—130.49 mm, MAE decreases by 2.52—129.84 mm, and MAPE decreases by 1.08—35.53 mm. The CEEMDAN-SE-ARIMA model has a better prediction effect in the northwest region, where the precipitation variability is small, and the prediction of the extreme value distribution center in the southeast region is more accurate.