## 5 Conclusions  
This study evaluated the performances of EPF and ESP for raw and postprocessed ensemble precipitation obtained from ECMWF and NCEP over a mountain river basin. The GPP and BMA methods were used to postprocess EPFs and the XAJ model was used in building ESPs. Both the deterministic and probabilistic metrics, MAE and CRPSS, were chosen to evaluate the forecasting performances. The following conclusions can be drawn:  
1. Raw ECMWF shows a better performance in EPF than raw NCEP in terms of lower MAE and higher CRPSS at all 7 lead days. Raw ECMWF also shows a better performance in ESP with high skill for 1-3 lead days, and both magnitudes and peak occurrence time of peak events were captured better.
2. The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances.
3. Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, MAE values are lower than 4.2 and CRPSS values are higher than 0.43 for both ECMWF and NCEP EPFs at all 7 lead days. The GPP-ECMWF ESP is highly skillful for 1-5 lead days and the GPP-NCEP ESP is, on average, moderately skillful for 1-7 lead days. In addition, the skillful forecast lead time can be 3 days/2 days for GPP-ECMWF/GPP-NCEP flood predications, with absolute differences in magnitude of less than 15% for peak events.  
Figure 11: Hydrographs of ensemble streamflow simulated by XAJ model using raw and postprocessed EPFs from NCEP as inputs at 1, 2, 3, and 4 lead days, compared with observed streamflow. The shaded area represents the 5 th–95 th percentile values driving by 1000 postprocessed ensembles.  
Overall, this study revealed the potential of ECMWF and NCEP in medium-term precipitation and streamflow forecasting over a mountain river basin and showed the effectiveness of the GPP method in improving forecast skill. There are still some limitations. Previous studies [28; 39] showed that the ensemble of multiple EPFs based on combination methods exhibited better forecast skill than the single EPF. Therefore, further work is needed to investigate the forecast skill of the ensemble of multiple EPFs. In addition, only one hydrological model was adopted for streamflow forecasting, even though hydrological prediction accuracy is influenced by different hydrological models [25; 28]. This lack of consideration is because the purpose of this study is mainly to investigate the ESP performances related to different precipitation inputs and postprocessing methods. The uncertainty in streamflow prediction related to hydrological models will be investigated in our future study.  
Conceptualization, T.P.; Data curation, Y.X. and T.S.; Formal analysis, Y.X.; Funding acquisition, T.P.; Investigation, Y.X.; Methodology, Y.X. and Q.G.; Resources, Q.G. and H.Q.; Validation, T.S. and H.Q.; Writing--original draft, Y.X.; Writing--review & editing, Y.X. and T.P. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Key Research and Development Program of China (2018 YFC1507505, 2018 YFC1507204), the Special Program for Innovative Development of China Meteorological Administration (CXFZ22J08, CXFZ2022J019), the Open Grants of the State Key Laboratory of Severe Weather (2021 LASW-A03), Key Research Projects of Hubei Meteorological Bureau (2022Y26, 2022Y06).  
Not applicable.  
The publicly archived observed meteorological dataset can be accessed from China Meteorological Administration. The observed streamflow data can be accessed at the Shuibuya hydropower station. The Publicly available datasets ECMWF and NCEP analyzed in this study can be found here: [[https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/](https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/)]([https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/](https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/)).  
The authors declare no conflict of interest.