Improved and Expanded Probabilistic Downscaling of CMIP6

Objective:

Development of input/forcing datasets of future climate change. Specifically precipitation and temperature

Abstract:

The goal of this project is to develop a gridded dataset of future hourly and daily precipitation
for the entire U.S. and U.S. territories out to the year 2100. A state-of-the-art statistical
downscaling methodology which translates global climate model output to realistic high-
resolution gridded precipitation will be used. Each climate model from the Coupled Model
Intercomparison Project Phase 6 (CMIP6) will be downscaled individually so that the degree of
uncertainty in future projections can be properly accessed. Sophisticated methods for
accounting for topography and recent advancements in machine learning will be used to
increase the skill and accuracy of the downscaling. The downscaling methodology is well suited
for reproducing precipitation extremes and will provide valuable information on future point
precipitation frequency estimates. These estimates are important for the design stormwater
management systems, drainage systems, flood control measures, and other infrastructure to
handle extreme rainfall events.