As the climate continues to change, it is important to understand how rising temperatures and frequent intense storm events affect the built infrastructure and societal resources in the future. Global and regional climate models provide projections of future climate but these do not represent future weather because of their coarse spatial resolution. Yet scientific models for hydraulic structures for example require weather inputs at the daily to subdaily temporal scale and from a single location to a watershed scale. Statistical Downscaling (SD) provides the translation between projected climate and potential future weather scenario at the local level. Numerous methods for SD have been proposed, but the variability of estimation is often not accounted for, nor are most methods capable of replicating the statistical structure of local weather. By embedding the SD in a statistical estimation framework, such as time series modeling, and generalized linear modeling, we are able to account for the statistical uncertainty. Lastly, by extending the more traditional models using “machine learning” models, we are able to further reduce the uncertainty of estimating future weather scenarios. We illustrate the methods with examples from the freeze-thaw cycle and from regional precipitation analysis.
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