Abstract: In this study, a novel hybrid model, named as ES-RELM, based on ensemble empirical mode decomposition (EEMD), sample entropy (SE), and regularized extreme learning machine (RELM), is developed for daily runoff forecasting featured with nonlinearity and non-stationarity. To extract more reliable information from runoff time series, EEMD-SE is used to decompose the runoff series to a set of sub-series with different complexity, then each sub-series is forecasted independently by a different RELM model, and finally all the sub-series forecasts are combined into an overall forecast of the runoff time series. It is applied in a case study to forecast the daily runoff at Pingshan station, a control station of the lower Jinsha River, and compared in detail with nine other models. Results indicate that our ES-RELM effectively improves the accuracy of daily runoff forecasting and is an efficient, stable forecasting model, thus laying a basis for high-precision real-time runoff forecasting.