Abstract

Statistical downscaling (SD) methods used to refine future climate change projections produced by physical models have been applied to a variety of variables. We evaluate four empirical distributional type SD methods as applied to daily precipitation, which because of its binary nature (wet vs. d... Show moreStatistical downscaling (SD) methods used to refine future climate change projections produced by physical models have been applied to a variety of variables. We evaluate four empirical distributional type SD methods as applied to daily precipitation, which because of its binary nature (wet vs. dry days) and tendency for a long right tail presents a special challenge. Using data over the Continental U.S. we use a 'Perfect Model' approach in which data from a large-scale dynamical model is used as a proxy for both observations and model output. This experimental design allows for an assessment of expected performance of SD methods in a future high-emissions climate-change scenario. We find performance is tied much more to configuration options rather than choice of SD method. In particular, proper handling of dry days (i.e., those with zero precipitation) is crucial to success. Although SD skill in reproducing day-to-day variability is modest (similar to 15-25%), about half that found for temperature in our earlier work, skill is much greater with regards to reproducing the statistical distribution of precipitation (similar to 50-60%). This disparity is the result of the stochastic nature of precipitation as pointed out by other authors. Distributional skill in the tails is lower overall (similar to 30-35%), although in some regions and seasons it is small to non-existent. Even when SD skill in the tails is reasonably good, in some instances, particularly in the southeastern United States during summer, absolute daily errors at some gridpoints can be large (similar to 20 mm or more), highlighting the challenges in projecting future extremes. Show less