Abstract: In this study, discrete wavelet transform (DWT) and a generalized regression neural network (GRNN) were integrated to forecast monthly runoff and improve the accuracy of medium- and long-term hydrologic forecasting models. First, DTW was used to decompose the runoff series into deterministic and stochastic components, then these two components were inputted into two different GRNN models respectively, and finally the prediction results of the two models were summed up as the final forecasts of monthly runoff. To estimate the forecasting accuracy of this superposition model, we compared it with the best model taking only the deterministic component as GRNN input and the traditional GRNN model without DWT, in terms of three indexes: mean absolute error (MAE), determination coefficient (DC), and correlation coefficient (R). Its application to the monthly runoff of the Yingluoxia station at the Heihe River shows that it has an accuracy slightly higher than that of the best single component model, but these two models are more accurate than the traditional GRNN. Thus, GRNN coupled with DWT improves the accuracy of monthly runoff forecasting and is useful for runoff prediction in practice.