TIGGE offers an opportunity to develop methods of applying global ensemble predictions systems (EPSs) from different models to improve the predictions of local hydrological risks. Comparative assessments of data from different EPSs improve the application of the grand ensemble forecasts from multiple sources. Based on the observed rainfall records from 10 stations in the Yishu River basin during August 2007, ensemble forecasts of 6-h precipitation from 5 EPSs, i.e., BABJ (Beijing), ECMF (ECWMF), EGRR (UKMO), RJTD (Japan), and KWBC (NCEP), are compared and assessed using several quantitative methods. Comparing the ensemble mean precipitation rates of all the EPS, EGRR scored highest with a correlation coefficient (R) of 0.48 and Nash-Sutcliffe efficiency (NE) of 0.21 in the lead time of four days; BABJ scored the lowest. For the control predictions of all the EPSs, RJTD scored highest with R = 0.19 and NE = 0.13 in a lead time of four days, followed by BABJ and EGRR. Compared with the control predictions, the ensemble mean of each EPS showed better performance. The multimodal ensemble mean showed a further improvement of the prediction skill. In the lead time of four days the multimodal ensemble mean had an R = 0.49 and NE=0.24. For different threshold values, the analysis of the threat score (TS) and Brier score (BS) showed similar comparative assessment. When the prediction lead time was increased, all the EPS showed a stable decline of prediction skills, with EGRR showing the longest and most stable decline period (9 days).