This study focus on the assessment of climate simulation variables by exploiting different bias correction methods and analysis the comparison of different bias corrected simulation. Because measured models need to generate high resolution input data, global and regional climate models are often distorted and have poor resolution. The purpose of this study is to find an accurate bias correction approach for climate variables prediction which are used to estimate the potential of renewable energy in a local area. Three different bias correction methods are analyzed, one of them is simply to correct based on mean values of previous data while others are based on the choice of different quantiles.