HAPpy Hour Seminar: Evaluation of Statistical Downscaling Methods for Regional Precipitation and Temperature Over CONUS

Abstract: 
Projecting the impacts of future climate change is crucially important to water managers planning to mitigate those impacts. Unfortunately, global climate model output is generally too coarse to provide the detailed, localized information necessary to prepare for those impacts, and the extreme computational cost to run more finely gridded models for a long enough time period can be prohibitive. Statistical downscaling methods are a relatively inexpensive way to refine those climate model outputs into a more useful form. Here, we evaluate multiple statistical downscaling methods applied to six CMIP5 models against multiple gridded observational datasets across our metric suite to determine the efficacy of these methods to properly capture precipitation and temperature means and variability. We compute several traditional metrics assessing seasonal means, ENSO teleconnection patterns, and the spatial characteristics of extremes, as well as develop new metrics based on a K-means weather typing algorithm to gain a more complete understanding of how these downscaled climate datasets compare to observations under various large-scale meteorological conditions.

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