Statistical downscaling of regional climate model output to achieve projections of precipitation extremes

Abstract

In this work we perform a statistical downscaling by applying a CDF transformation function to local-level daily precipitation extremes (from NCDC station data) and corresponding NARCCAP regional climate model (RCM) output to derive local-scale projections. These high-resolution projections are essential in assessing the impacts of projected climate change. The downscaling method is performed on 58 locations throughout New England, and from the projected distribution of extreme precipitation local-level 25-year return levels are calculated. To obtain uncertainty estimates for return levels, three procedures are employed: a parametric bootstrapping with mean corrected confidence intervals, a non-parametric bootstrapping with BCa (bias corrected and acceleration) intervals, and a Bayesian model. In all cases, results are presented via distributions of differences in return levels between predicted and historical periods. Results from the three procedures show very few New England locations with significant increases in 25-year return levels from the historical to projected periods. This may indicate that projected trends in New England precipitation tend to be statistically less significant than suggested by many studies. For all three procedures, downscaled results are highly dependent on RCM and GCM model choice.

Keywords

• Statistical downscaling
• Generalized Pareto distribution
• Regional climate models
• Bootstrapping
• Bayesian analysis
• Uncertainty quantification