Evaluation of some distributional downscaling methods as applied to daily precipitation with an eye towards extremes

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. 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 (~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 (~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 (~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 (~20 mm or more), highlighting the challenges in projecting future extremes.

Evaluation of Bias-Corrected GCM CMIP6 Simulation of Sea Surface Temperature over the Gulf of Guinea

“…The motivation for this research originates basically from the need to accurately understand and address future climate impacts in Africa both at the regional and local scales and the imperative for climate data products to represent climate extremes accurately and be available at fine and temporal resolutions [47]. This requires the deployment of bias correction techniques to rectify the problem associated with GCM simulations to generate reliable information at a local scale, which can be used for the formulation of climate adaptation strategies in Africa in the face of the increased frequency and severity of climate extremes.…”

Robust Bias-Correction of Precipitation Extremes Using A Novel Hybrid Empirical Quantile Mapping Method: Advantages of A Linear Correction For Extremes

“…Model reliability is largely dependent on the quality and resolution of climate data products [18], [35], [21] [17]. The representation of extremes, in particular, can have a disproportionately large effect on such models [46]. Increases in the frequency, variability, and magnitude of extreme precipitation over the last several decades, especially in the northeastern United States, are welldocumented [31] [39].…”

“…Increases in the frequency, variability, and magnitude of extreme precipitation over the last several decades, especially in the northeastern United States, are welldocumented [31] [39]. To study the future impacts of changing extremes at local scales, climate data products must represent extreme events accurately and be available at fine spatial and temporal resolutions [46]. General circulation models (GCMs) provide important information about historical and future larger-scale climate trends, but their resolution is too coarse to investigate localized effects of changes in extreme climate events [13], [45].…”