Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. We produced a set of regional climate projections that have the systematic biases reduced. The dataset first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the globally-simulated variables that are subsequently used to drive the regional model. The bias-corrected global simulation data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE) in comparison to the original globally-driven simulation. This effective and economical method provides a useful tool to reduce biases in regional climate downscaling simulations of water resource variables.