Bias correction techniques have shown great promise in climate modelling and impact assessment studies in recent years. The climate model data should be corrected /downscaled for inaccuracies and prediction uncertainties using efficient bias correction techniques. Therefore, this study was focused on evaluating bias correction methods for downscaling CMIP6 global climate models precipitation simulations in selected stations of Ethiopia. The five precipitation bias correction methods that were applied in this study include linear scaling (multiplicative), delta change correction (multiplicative), precipitation local intensity scaling, power transformation of precipitation and distribution mapping of precipitation. Daily precipitation data for the selected stations were obtained from the National Meteorological Service Agency (NMSA) of Ethiopia, whereas the CMIP6 GCM data was a historical CNRM CM6 1 (Centre National de Recherches Météorologiques) model obtained from 1995 to 2014. The model data was extracted and bias corrected with observation at CMhyd tools. The bias correction methods were evaluated with their CRI (comprehensive rating index) values and several statistical measures. This study revealed that, delta change correction (multiplicative) was performed better than the other bias correction methods for all selected stations at alltime scales. It has perfect correlation of 1, a bias of zero percent, and low errors in simulation. Power transformation and distribution mapping were the best preferable methods next to delta change correction in daily and monthly simulations. All bias correction techniques were adequately downscaled the GCM in mean monthly precipitations simulations for almost all selected stations. The power transformation and linear scaling techniques were effective to adjust the annual GCM simulation. Whereas precipitation local intensity scaling and linear scaling multiplicative bias correction methods were less preferable than the above methods at daily and monthly period. The distribution mapping of precipitation method led to underestimating and overestimating the given observed precipitation in all periods,