Rodrigo Manzanas

TL;DR: An updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher model resolution, and the generation of a new dataset with monthly temperature and precipitation spatially aggregated in the new regions.

TL;DR: In this paper, the performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods.

TL;DR: An intercomparison of a large ensemble of statistical downscaling methods over Europe is presented in this article, where the authors compare the results from the VALUE perfect predictor cross-validation experiment.

TL;DR: A comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework, shows that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered.