Abstract: The runoff process is a vital part of the earth’s hydrological cycle. Scientific and accurate prediction of monthly runoff inflow is of great significance for water flow scheduling, water resources planning and management in the basin. However, due to the complexity of the runoff process and the influence of human activities, it is very difficult to accurately capture the variation law of the monthly runoff time series in a changing environment. Because of the two problems in the prediction of monthly runoff time series, the prior information identification in the sample data is not thorough enough, and the time step embedding dimension is difficult to be effectively and adaptively selected.This paper designs a model for monthly runoff time series based on VMD-PSR-BNN. Based on the good robustness of variational mode decomposition（VMD） algorithm to noise and the characteristics of accurate decomposition of time series signals, the monthly runoff time series is regarded as a time series signal, and the VMD method is used to decompose the monthly runoff time series into multiple relatively stationary intrinsic mode function（IMF）. Then, each IMF is reconstructed based on the phase space reconstruction（PSR） theory, and the Bayesian neural network（BNN） based on variational inference is used to predict each reconstructed IMF. Finally, the prediction results of each BNN are aggregated and reconstructed to obtain the final prediction result. The monthly runoff time series from 1953 to 2018 of two hydrological stations in Xianyang and Huaxian in the Weihe River Basin are selected for case analysis. The results show that the prediction results of the two hydrological stations based on the VMD-PSR-BNN model can reach the first-class standard of hydrological forecasting, and have a good fitting effect for the extreme values in the sample, which provides a new method reference for the prediction of monthly runoff time series.