Advantages and Limitations of Using Downscaled Climate Data in Extreme Rainfall Projections
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In a warming climate, increases in intensity and frequency of extreme rainfall events are being observed around the globe. To better quantify future risks associated with climate change in flood resilience and adaptation efforts, practitioners increasingly reach for downscaled future climate model data to be used in stormwater and transportation infrastructure redesign. Global climate models provide information at coarse spatial and temporal scales, insufficiently simulating atmospheric processes and extreme events to support regional and local decision making. Data at finer spatial and temporal resolutions often originate from either observation-driven statistical associations (statistical downscaling) or complex, physics-based models (dynamical downscaling).
In this study we evaluate extreme rainfall representation in different downscaling techniques (statistical and dynamical), at different spatial (36km, 25km, 4km) and temporal (hourly and daily) resolutions of future rainfall datasets and assess the impact of averaging over multiple-member ensembles. Additionally, factors such as performance of global climate models which come with inherent uncertainty, effect of bias correction and relevance to specific study location are considered. The study will illustrate advantages and limitations of downscaled simulations on different regional scales: CONUS, climate regions, and watershed. Provided synthesis of the factors impacting extreme rainfall projections will guide practitioners in choosing the appropriate data for their decision-making applications.