Empirical Statistical Downscaling (ESD) is an umbrella term for methods that statistically relate local expressions of the climate system, such as temperature or precipitation recorded at weather stations, to larger-scale atmospheric conditions, such as the Antarctic Oscillation (AAO) and El Niño/Southern Oscillation (Fig. 1). For example, coarse climate model output with location-dependent systematic errors in rainfall may be fitted to local-scale observational data of rainfall. The resulting statistical model may then use these large-scale conditions as predictors for local-scale rainfall. This method is well established in the climatological and meteorological community. It is (computationally) less expensive than dynamical downscaling based on regional climate models, and takes in-situ geographical realities, such as local topography, into consideration without the need to explicitly parameterise these.
While dynamical downscaling over South America has significantly advanced in the past two decades, the ESD community in South America is incipient and the potential for ESD in the region, as demonstrated by the dense network of records (Fig. 2), has not been explored as thoroughly as in other parts of the world. The focus therefore lies on developing and applying models for statistical downscaling in Chile.