Easy access to accurate and reliable climate data is a crucial concern in hydrological modeling. In this regard, gridded climate data have recently been provided as an alternative to observational data. However, those data should be first evaluated and corrected to guarantee their validity and accuracy. This study offered a new approach to assess the ECMWF gridded precipitation data based on some indicators including, correlation coefficient (CC) and normalized root-mean-square error (NRMSE), and absolute error (AE) in daily and monthly intervals (2007–2017) across different climatic and geographical areas of Iran. Besides, an artificial neural network (ANN) model was utilized to correct the ECMWF precipitation product. According to the results, NRMSE was less than 2 (in 93% of stations) and 5 (in63% of stations) on monthly and daily scales, respectively. Moreover, CC was above 0.6 in 58% and 94% of stations on daily and monthly scales, respectively.The AEs values were between -0.5 to 0.5, in 80% (daily scale) and 50% (monthly scale) of stations. Having corrected the ECMWF precipitation product by ANN, the number of stations with NRMSE<5 increased from 63% to 74% on a daily scale, whereas the number of stations with NRMSE<2 reached="" from="" on="" a="" monthly="" scale.="" the="" results="" also="" showed="" that="" number="" of="" stations="" with="" cc="">0.6 increased from 58% to 87% on a daily scale.