One of the downscaling procedures used in this project involves a unique approach for downscaling daily precipitation extremes from historical analogs. Unlike previous analog downscaling methods, the new method utilizes a multi-step procedure in which the occurrence of extreme precipitation on a given future target day is first determined based on the probability of extreme precipitation on that day's most similar historical analog days. If extreme precipitation occurred on the selected analog day(s), daily precipitation observations recorded at stations on the analog day(s) are used to ascribe precipitation amounts on the corresponding future target day. The method was developed and tested for a historical trial period using daily precipitation data from 157 Cooperative Observer Program (COOP) stations and National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data.
The performance of the analog method was tested by conducting a forecast verification and comparing analog-derived precipitation extremes with observed precipitation extremes. Overall, results were generally positive and indicate that the proposed analog method is suitable for downscaling daily precipitation extremes in New York State. While the analog method slightly underestimates the occurrence of extreme precipitation, it presents a significant improvement over climatology in terms of accurately predicting the occurrence of extreme precipitation on a given day. Return period precipitation amounts estimated from the analog method are similar to, but generally lower than those calculated from daily precipitation observations. Over the entire study domain, the median difference between downscaled and observed return period precipitation amounts is less than 10%. These precipitation biases compare favorably with those obtained from historical dynamically downscaled climate model simulations.