## 1 Introduction  
The accurate and early forecast of river streamflow, as well as the possible uncertainties in it, have the prowess of providing critical information for water resource management and disaster mitigation [1; 2; 3]. Ground observations are the traditional sources of precipitation for hydrological applications, which can only provide short-term flood forecasts with a lead time of 1-24 h. Single-value deterministic precipitation forecasts provided by numerical weather prediction (NWP) centers are also popular for streamflow forecasting, which can provide extended flood warnings but lack uncertainty information. Building an Ensemble Streamflow Prediction (ESP) system by simulating a calibrated hydrological model using ensemble precipitation forecasts (EPFs) is one useful methodology to meet these requirements [4]. To accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts, the Observing System Research and Predictability Experiment (THORPEX) project was proposed in 2003. The THORPEX Interactive Grand Global Ensemble(TIGGE) [5], a key component of THORPEX, has collected ensemble forecasts generated by more than 10 NWP centers around the world since 2006 [6]. Among them, the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) are more popular worldwide [7; 8; 9]. The successful applications of the TIGGE database before 2009 have been comprehensively reviewed by [PERSON] and [PERSON] [10]. Plenty of studies have evaluated ESP systems (ESPs) for flood forecasting and found that ESPs could provide desirable forecasts up to 10 lead day [4; 11; 12]. However, raw EPFs often contain systematic biases and spread deficiencies, as well as coarse spatial resolutions, which cannot directly drive hydrological models for ESPs [13; 14; 15]. Therefore, statistical postprocessing is a requisite to reduce biases and correct dispersion errors for raw EPFs [16]. To address this problem, various methods have been proposed, such as the Bayesian Model Averaging (BMA) [17], the Extended Logistic Regression (ExLR) [18], and the Generated based Postprocessing method (GPP) [19], etc. Numerous studies have assessed the performances of different postprocessing methods from the point of view of either meteorology [20; 21] or hydrology [9; 22]. [PERSON] and [PERSON] [23] compared the raw ECMWF ensemble forecasts and forecasts postprocessed with either a modified BMA method or the ExLR method in the Netherlands. The results showed that both the modified BMA and the ExLR could improve the raw ECMWF ensemble forecasts significantly for the first 5 forecast days. [PERSON], [PERSON] [24] applied the postprocessed precipitation forecast to drive a calibrated hydrological model to predict ensemble streamflow. They found that both the forecast reliability and sharpness of ensemble streamflow forecasts showed improvements. [PERSON], [PERSON] [25] evaluated NCEP ensemble forecasts corrected by GPP in a southern basin of China and found that the corrected NCEP could provide desirable forecasts up to 9 and 5 lead days for precipitation and flood season streamflow, respectively. Although there are lots of studies concentrating on precipitation forecasts [26; 27] and streamflow predictions [10; 28] for TIGGE and the effectiveness of postprocessing methods [5; 29; 30], few studies explore both the performances of TIGGE ensemble precipitation forecasts and their applicability in streamflow predictions over mountain river basins. [PERSON], [PERSON] [27] investigated the performances of the raw and postprocessed ensemble forecasts of heavy precipitation in mountainous areas, while they did not investigate their performances in ensemble streamflow prediction. This study aims to evaluate raw and postprocessed EPFs and assess the degree of improvement with different postprocessing methods to verify the reliabilities of EPF and ESP in a western mountain basin of China.