A comparison of techniques for downscaling extreme precipitation over the Northeastern United States

Accurate estimates of local changes in extreme precipitation are valuable for informing local policy decisions and estimating potential impacts on areas such as health, infrastructure, ecosystems and agriculture. To bridge the gap between the coarse spatial resolution of climate model output and the need for weather and climate information at a higher resolution, downscaling methods have been developed. This research compares several different downscaling methods in their ability to simulate rainfall extremes over the Northeastern United States. The methods compared are a bias-correction and spatial disaggregation technique, the statistical downscaling model (SDSM) and a regional climate model, HadRM3. Validation was done using generalized extreme value (GEV) distributions of annual maxima and return-period estimates. All the different techniques simulated reasonable estimates of extreme rainfall across the northeast. However, the agreement of simulations with observations depended upon the downscaling technique used and the location. The SDSM simulations matched observed extreme climatology the best. HadRM3 tended to overestimate mean precipitation and gave a GEV distribution that did not replicate the observed distribution well and, thus, overestimated the extremes. We then developed high-resolution projections of future changes to rainfall across the Northeast United States, using IPCC SRES emission scenario A2 combined with these downscaling methods. Future projections based on SDSM indicate increases in the magnitude of extreme rainfall events by similar to 7% through the 2041-2060 period with considerable regional variation particularly at the higher 50- and 100-year return periods. However, these projected increases rarely fall outside the 95% confidence intervals based on the observation period from 1961 to 1980. Copyright. (C) 2010 Royal Meteorological Society