Urban water supply is a nonlinear and non-stationary time series, and the combination forecasting model can get more accurate results. Through in-depth analysis of chaotic local-region method and neural network prediction model, this paper puts forward a new combination forecasting model, which uses chaotic local-region method to make a preliminary forecast for urban daily water supply, and then the prediction result is updated by neural network. The proposed combined model makes use of complementary advantages of the chaotic local-region method and the neural network, improving synchronously the accuracy and computational efficiency of the prediction results. To verify the proposed model, the prediction accuracy of the four single prediction models of Chaotic local-region method,BPNN, RBF and GRNN neural network and three corresponding combined models are analyzed quantitatively using seven years water supply data. The results show that combination forecasting model is of higher accuracy than single prediction model, and chaotic local-region method plus GRNN neural network combination model has highest accuracy with much lower computation time than single neural network predication model.