LAUSR

Statistical downscaling methods are extensively used to refine future climate change projections produced by physical models. Distributional methods, which are among the simplest to implement, are also among the most widely used, either by themselves or in conjunction with more complex approaches. Here, building off of earlier work we evaluate the performance of seven methods in this class that range widely in their degree of complexity. We employ daily maximum temperature over the Continental U.S. in a “Perfect Model” approach in which the output from a large‐scale dynamical model is used as a proxy for both observations and model output. Importantly, this experimental design allows one to estimate expected performance under a future high‐emissions climate‐change scenario. We examine skill over the full distribution as well in the tails, seasonal variations in skill, and the ability to reproduce the climate change signal. Viewed broadly, there generally are modest overall differences in performance across the majority of the methods. However, the choice of philosophical paradigms used to define the downscaling algorithms divides the seven methods into two classes, of better versus poorer overall performance. In particular, the bias‐correction plus change‐factor approach performs better overall than the bias‐correction only approach. Finally, we examine the performance of some special tail treatments that we introduced in earlier work which were based on extensions of a widely used existing scheme. We find that our tail treatments provide a further enhancement in downscaling extremes.