Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast

► We evaluate the importance of bias correction for both GCMs and RCMs. ► Bias correction of model outputs is necessary before their use in impact studies. ► Statistical downscaling offers an efficient tool for regional impact studies. ► SDBC approach successfully reduces the variability among model predictions.