Downscaled Projections of Extreme Precipitation in New York State

Future changes in extreme precipitation will have profound implications for agriculture, public infrastructure, and human health. According to the most recent assessment from the Intergovernmental Panel on Climate Change (IPCC), both the frequency and magnitude of extreme precipitation are expected to increase in midlatitude continental regions throughout the twenty-first century. Such changes will likely exacerbate the societal impacts of extreme precipitation in the future. In consideration of these issues, the Northeast Regional Climate Center (NRCC) has partnered with the New York State Energy Research and Development Authority (NYSERDA) to compare various methods of downscaling global climate model (GCM) output and create extreme precipitation projections for New York State. Primary objectives of this research include: 1) evaluation of downscaling method–climate model combinations to assess their ability to replicate present-day precipitation extremes, 2) downscaling of projected precipitation extremes for future time periods, and 3) quantification of methodological and climate model uncertainties.

Three different downscaling methods were used to estimate future daily precipitation extremes at 157 National Weather Service (NWS) Cooperative Observer Program (COOP) stations in New York State and portions of adjacent states and Canada. The first method employs quantile–quantile mapping to bias correct areally adjusted precipitation extremes obtained from dynamically downscaled climate model simulations. These simulations consist of regional climate models (RCMs) run at 50-km resolution and driven by atmosphere–ocean general circulation models (AOGCMs). The second method, a variation of the delta method, computes differences in simulated precipitation extremes between AOGCM future and historical periods, and simply applies these differences toward observed precipitation extremes. The third method combines quantile–quantile mapping with a unique approach for downscaling daily precipitation extremes from historical analogs. This analog approach involves a multi-step procedure in which the occurrence of extreme precipitation on a given target day is first determined based on the observed probability of extreme precipitation on that day's closest historical analog days. Then, if extreme precipitation occurred on the selected analog day(s), the precipitation observations associated with the historical analog day(s) are used to ascribe precipitation amounts on the corresponding target day.