Spatial Similarity and Transferability of Analog Dates for Precipitation Downscaling over France

Abstract

High-resolution weather scenarios generated for climate change impact studies from the output of climate models must be spatially consistent. Analog models (AMs) offer a high potential for the generation of such scenarios. For each prediction day, the scenario they provide is the weather observed for days in a historical archive that are analogous according to different predictors. When the same “analog date” is chosen for a prediction at several sites, spatial consistency is automatically satisfied. The optimal predictors and consequently the optimal analog dates, however, are expected to depend on the location for which the prediction is to be made. In the present work, the predictor (1000- and 500-hPa geopotential heights) domain of a benchmark AM is optimized for the probabilistic daily prediction of 8981 local precipitation “stations” over France. The corresponding 8981 locally domain-optimized AMs are used to explore the spatial transferability and similarity of the optimal analog dates obtained for different locations. Whereas the similarity is very low even when the locations are close, the spatial transferability of the optimal analog dates for a given location is high. When they are used for the prediction at all other locations, the loss of prediction performance is therefore very low over large spatial domains (up to 500 km). Spatial transferability is lower in the presence of high mountains. It also depends on the parameters of the AM (e.g., its archive length, predictors, and number of analog dates used for the prediction). In the present case, AMs with higher prediction skill exhibit lower transferability.