Dynamical downscaling is a promising tool to assess the future fate of water in a catchment. To overcome strong biases in Coupled Ocean-Atmosphere General Circulation Models (CGCMs), the Pseudo Global Warming Downscaling (PGW-DS), which combines climatology differences (future-past) of CGCM ensembles and Reanalysis Dataset (RD) was proposed as a reliable and efficient method, but downscaling of RD at a basin scale was not assessed. Hence, this study evaluated the results from a long-term, high-resolution Downscaled Precipitation (DP) derived from a RD over the Tone river basin. DP showed strong bias over mountainous regions owing to resolution enhancement. DP and CGCM precipitations were merged with Statistical Bias Correction methods (SBC) to obtain a comprehensive outlook on biases and the most appropriate SBC. Characteristics of biases were quite different in DP and CGCM, which performed very poor for extreme rainfall intensities and thus required an explicit treatment. Seasonal inconsistency in extreme events of CGCMs affected the corrected rainfall and discharges significantly. Conversely, a simple cumulative gamma method was successful for DP to represent climatology, extreme statistics of rainfall and discharges owing to the use of RD, which generated lesser bias compared to CGCMs. The proposed method will be applied to PGW-DS to obtain reliable information of future changes of water in the basin.