## 4 Results and Discussion
### Performance of the Ensemble Streamflow Forecasts  
In this section, the XAJ model was firstly calibrated to river discharge data based on meteorological observation data over the 2014-2016 period and validated over the 2017 period. Then, the ESPs were built by driving the calibrated XAJ model using raw EPFs, and postprocessed EPFs from ECMWF and NCEP, respectively. Figure 7 presents the hydrographs of the daily simulated and observed streamflow for both calibration and validation periods; the NSE and RE are also present. It can be seen that the NSE values are both greater than 0.8 and the RE values are both below 10% for both the calibration and validation periods. In addition, the trend and magnitude of the simulated hydrograph are well-captured for both the peak and low flows. In general, the results demonstrate satisfactory performance of the XAJ model and thus can be used for streamflow forecasting. Figure 5: Monthly variation of evaluation indexes for ensemble precipitation forecasts by raw and postprocessed ECMWF (**a1,a2**) and NCEP (**b1,b2**). Figure 6: Relative operating characteristic (ROC) curve at 1, 3, 5, and 7 lead days for ECMWF (solid line) and NCEP (dash line) for events of precipitation greater than 50 mm (the FPR is the hit rate of no-event “0”, and the TPR is the hit rate of event “1”). In the calculation of ROC, the daily data of 2017 were used. Figure 7: Hydrographs simulated by XAJ model in calibration and validation periods, compared with daily observations. Figure 8 presents the scatter plots of the ensemble mean forecast and observed streamflow at 1-4 lead days during the 2017 period. Figure 8a1-a12 are for ECMWF, and (Figure 8b1-b12) are for NCEP. The figure shows larger biases for forecast streamflow with the lead days increasing compared with observed streamflow. Both ECMWF and NCEP underestimate high streamflow (>2000 m\({}^{3}\)/s), which may be due to the underestimation of heavy precipitation events by EPFs (Figure 3). The use of NSE for calibrating the hydrological model could also result in underestimating peak flow events, which was theoretically demonstrated by [PERSON], [PERSON] [45]. What's more, [PERSON], [PERSON] [46] pointed out that the XAJ model was built for the regions with low surface runoff and high interflows with the concept of runoff generation on depletion of storage. While in mountainous areas, a flood could happen when the intensity of rainfall is significant without filling up the soil storage, thus leading to the underestimation of peak flow events. As for ECMWF, the performance in forecasting streamflow with low and moderate amounts (<2000 m\({}^{3}\)/s) is better than that with high amounts, with the former closer to the best-fitting line. As for NCEP, the performances are poor in forecasting streamflow with either high or low amounts, showing many data points far away from the best-fitting line. In addition, the improvements in depicting streamflow amounts are not significant for the forecasts by using the GPP method, while the performance is even worse when using the BMA method. Figure 8: Scatter plots of ensemble mean forecast and observed streamflow during the 2017 period. (**a1–a12**) represent ECMWF and (**b1–b12**) represent NCEP. Columns 1–4 represent 1, 2, 3, and 4 lead days, respectively. Table 2 presents the CRPSS values for ensemble streamflow simulated by raw and postprocessed ECMWF and NCEP at 1\(\sim\)7 lead days over a 1-year period in 2017. ECMWF shows a better performance in streamflow prediction than NCEP, depicted by higher CRPSS values at all 7 lead days. According to studies of [PERSON], [PERSON] [47] and [PERSON], [PERSON] [48], the degree of ESP skill can be considered to be: very high if CRPSS is [0.75, 1]; high if CRPSS is [0.5, 0.75); moderate if CRPSS is [0.25, 0.5); low if CRPSS is (0, 0. 25); no skill if CRPSS = 0, and negative skill if CRPSS < 0. Based on that, the ESP skill of raw ECMWF is high for the first 3 lead days, and it is moderate for the latter 4 lead days.