Statistical downscaling with artificial neural networks

Statistical downscaling methods seek to model the relationship between large scale atmospheric circulation, on say a European scale, and climatic variables, such as temperature and precipitation, on a regional or subregional scale. Downscaling is an important area of research as it bridges the gap between predictions of future circulation generated by General Circulation Models (GCMs) and the effects of climate change on smaller scales, which are often of greater interest to end-users. In this paper we describe a neural network based approach to statistical downscaling, with application to the analysis of events associated with extreme precipitation in the United Kingdom.