Edit to the article format, all the references should be given according to the text. “Precipitation is one of the most important atmospheric phenomena, and improving precipitation predictions is one of the main goals and the major challenges of climate prediction centers (Fritch et al., 1998). More accurate predicting of precipitation is significantly important in management of issues such as drought, hydrology, agricultural meteorology, and flood forecasting. One of the basic tools in predicating various meteorological quantities is Numerical Weather Prediction (NWP) models, which have made significant progress in weather climate prediction. However, it is very difficult to avoid biases due to model uncertainties, such as physical processes and insufficient resolution (Yoshikane et al., 2022). Two of the most important sources of error are the initial condition error and model error (Privé et al., 2013, Bonavita et al., 2020). In recent years, it has become common to use Ensemble Prediction Systems (EPS) to consider uncertainties. Instead of a single deterministic prediction, the EPS consists of several predictions resulting from changes in the initial conditions and in the model physics. Despite the almost high skill of ensemble forecasts, these forecasts suffer from under-dispersion and not calibrated, especially for meteorological parameters near ground level (Vannitsem et al., 2018). To overcome this issue, several post-processing methods have been developed (e.g., Wilks 2006; Wilks and Hamill 2007; Williams et al., 2014), that the most popular ones are Bayesian Model Averaging (BMA; Raftery et al. 2005; Sloughter et al., 2007; Soltanzadeh et al., 2011; Baran, 2014) and Ensemble Model Output Statistics (EMOS; Gneiting et al. 2005; Wilks and Hamil, 2007; Thorarinsdottir and Gneiting, 2010; Scheuerer, 2014; Baran and Lerch, 2015; Scheuerer and Hamill 2015).”