We evaluate a machine learning model to be used when downscaling regional long term variability impacts on hydrology and other applications. We show that different downscaling approaches matter more when you look at smaller watersheds, that coupling the land surface to the atmosphere is important for regional downscaling, and that some machine learning architectures can struggle with that coupling.