###### Abstract  
Ensemble precipitation forecasts (EFs) can help to extend lead times and provide reliable probabilistic forecasts, which have been widely applied for streamflow predictions by driving hydrological models. Nonetheless, inherent biases and under-dispersion in EPFs require post-processing for accurate application. It is imperative to explore the skillful lead time of post-processed EPFs for summer streamflow predictions, particularly in mountainous regions. In this study, four popular EPFs, i.e., the CMA, ECMWF, JMA, and NCEP, were post-processed by two state of art methods, i.e., the Bayesian model averaging (BMA) and generator-based post-processing (GPP) methods. These refined forecasts were subsequently integrated with the Xin'anjiang (XAJ) model for summer streamflow prediction. The performances of precipitation forecasts and streamflow predictions were comprehensively evaluated before and after post-processing. The results reveal that raw EPFs frequently deviate from ensemble mean forecasts, particularly underestimating torrential rain. There are also clear underestimations of uncertainty in their probabilistic forecasts. Among the four EPFs, the ECMWF outperforms its peers, delivering skillful precipitation forecasts for 1-7 lead days and streamflow predictions for 1-4 lead days. The effectiveness of post-processing methods varies, yet both GPP and BMA address the under-dispersion of EPFs effectively. The GPP method, recommended as the superior method, can effectively improve both deterministic and probabilistic forecasting accuracy. Moreover, the ECMWF post-processed by GPP extends the effective lead time to seven days and reduces the underestimation of peak flows. The findings of this study underscore the potential benefits of adepthy post-processed EPFs, providing a reference for streamflow prediction over mountain river basins. ensemble forecasts; precipitation post-process; Bayesian model averaging (BMA); generated-based post-processing (GPP); summer streamflow prediction; mountainous river basin +
Footnote †: journal: _atmosphere_  
0000-0002-2000-2000-2000-2000-2000-2000-2000-2000-2000-2000-2000-20000-2000-2000-20000-2000-20000-20000-20000-20000-20000-20000-20000-20000-20000-200000-20000-200000-20000-200000-  
Today, ensemble forecasts are commonly made at most of the major operational weather prediction facilities worldwide. The National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), China Meteorological Administration (CMA), and Japan Meteorological Administration (JMA) are the four most popular NWP models. Their datasets have been widely used in both meteorology and hydrology studies ever since they were released [1; 8]. Over the past decade, many studies have evaluated the capability of EPFs in enhancing precipitation and streamflow forecasts [9; 10; 11]. However, NWP models often exhibit biases and under-dispersions, which stem from their inherent inability to perfectly capture atmospheric dynamics [12]. Such discrepancies become even more pronounced in EPFs during extreme events [13]. Therefore, numerous statistical post-processing methods have been proposed to reduce the bias and to form probability distributions of raw EWFs. These methods can be divided into two categories: distribution-based methods, such as Bayesian model averaging (BMA) [14; 15] and non-Gaussian regression (NGR) [16], and generator-based methods, such as modified extended logistic regression (ExLR) [17] and generator-based post-processing (GPP) [18]. The former needs to calibrate the probability distribution function (PDF) of the weather variable based on raw EWFs, the latter generates the forecast ensemble by conditionally resampling historical observations using the forecast information from raw EWFs. One of the most widely applied distribution-based methods is the BMA method, which can yield considerable improvements in precipitation forecasting skill [19; 20; 21].