 Water resources are essential to the ecosystem and social economy in the desert and oasis of the Dharim River Basin, northwest China. Expected to be vulnerable to climate change, it has been demonstrated that regional climate models, RCMs, provide more reliable results for a regional impact study of climate change on water resources, then general circulation models, GCMs. However, due to their considerable bias, it is still necessary to apply bias correction before they are used for water resources research. After a sensitivity analysis on input meteorological variables based on the SOBL method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations, applied over the Kaidu River Basin, one of the headwaters of the Dharim River Basin. Precipitation correction methods applied include linear scaling, LS, local intensity scaling, LOSI, power transformation, PT, distribution mapping, DM, and quantile mapping, QM, while temperature correction methods are LS, variance scaling, VARI, and DM. The corrected precipitation and temperature were compared to the observed meteorological data prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on. This article was authored by G.H. Fong, J. Yong, Y. N. Chen, and others. We are article.tv, links in the description below.