Abstract
Empirical statistical downscaling (ESD) under the perfect prognosis approach was carried out to simulate daily maximum (Tx) and minimum temperatures (Tn) in 101 meteorological stations over the different climatic regions of Argentina. To this end, three ESD families were evaluated: analogs (AN), generalized linear models (GLM) and artificial neural networks (ANN) considering a variety of predictor sets with multiple configurations driven by three different reanalyses (ERA, JRA, NCEP). ESD models were cross‐validated using folds of nonconsecutive years (1979–2014) and then evaluated in a warmer set of years (independent warm period, 2015–2018) to assess their extrapolation capability. Depending on the aspect analysed, AN, GLM or ANN models were more/less skilful, but no method fulfilled all the features of both predicand variables. In this sense, the predictor set and model configuration were key factors. For each ESD method, the different predictor structures (point‐wise, spatial‐wise and combinations of them) introduced the main differences, regardless of the predictand variable, region and reanalysis choice. However, some specific results could be highlighted. ERA (NCEP)‐driven ESD models were the most (least) skilful in representing Tx and Tn. In the case of Tn, models' skills considerably increased when humidity information was included in the predictor set. Our results showed that downscaling models were able to capture the general characteristics of Tx and Tn in all regions, with better performance in the latter variable. However, regions with complex topography (Argentinian Patagonia and the subtropical Andes) pose a further challenge for capturing the local variability of daily extreme temperatures. The performance of the ESD models in the atypical warm conditions was similar to the one during the cross‐validated period, showing some extrapolation skill. The results of this work set a reference for future ESD developments and comparisons in Argentina.