Abstract: Influenced by global climate change and human activities, the processes of rainfall and runoff show non-stationary characteristics that are increasingly remarkable. To provide necessary and useful decision making information, it is crutial to improve forecasting accuracy of the future changes in hydrological time series using more effective methods. Empirical mode decomposition (EMD) is a key technique of the decomposition-prediction-reconstruction approach. This paper decribes a modified radial basis function (RBF) prediction model that integrates EMD and the RBF neural network. Using this model, we have examined the efficiencies of such an approach in prediction of two non-stationary time series for the Wei River basin, a precipitation series showing weak-trend changes, and a runoff series showing strong-trend changes, and compared our modfied RBF method with the original RBF. In addition, a new measure was adopted to reduce errors in prediction of high-frequency components decomposed by EMD. The results show that for weak-trend precipitation series, RBF gave a satisfactory prediction with a mean relative error of 11% and the modified RBF behaved similar. For strong-trend runoff series, the modified RBF was better, reducing the errors from 54% to 30%. This error can be decreased further by 2% if using error control measures. The comparison shows that the modified RBF method is applicable to non-stationary time series featured with strong-trend changes and it can easily decompose a time series into random, periodic and trend components and extrapolate each of them effectively. But beyond that, error control measures improve the efficiency of a prediction approach and can be used as a general technique for non-stationary time series forecasting.