Daniel San-Martín

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: It is shown that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.

TL;DR: In this article, a new atmospheric-river detection and tracking scheme based on the magnitude and direction of integrated water vapour transport is presented and applied separately over 13 regions located along the west coasts of Europe (including North Africa) and North America.

TL;DR: This work has been funded by the Spanish R+D Program of theMinistry of Economy and Competitiveness, through grants MULTI-SDM and INSIGNIA, co-funded byERDF/FEDER.

TL;DR: In this article, the performance of statistical downscaling methods (SDMs) is critically reassessed with respect to their robust applicability in climate change studies, and a reduced ensemble of well-performing SDMs is applied to four GCMs to properly assess the uncertainty of downscaled future climate projections.