With the rapid development of artificial intelligence, machine learning is gradually becoming
popular for predictions in all walks of life. In meteorology, it is gradually competing with …

Statistical downscaling and bias correction are becoming standard tools in climate impact
studies. This book provides a comprehensive reference to widely-used approaches, and …

General circulation models (GCMs) allow the analysis of potential changes in the climate
system under different emissions scenarios. However, their spatial resolution is too coarse to …

Global climate models (GCMs) are the primary tools to simulate multi-decadal climate
dynamics and to generate global climate change projections under different future emission …

In this paper, we evaluate the performance of 8 statistical and machine learning methods,
driven by atmospheric synoptic patterns, for long-term daily rainfall prediction in a semi-arid …

It is becoming increasingly crucial to develop methods and strategies to assess building
performance under the changing climate and to yield a more sustainable and resilient …

Deep Learning (DL) has recently emerged as an innovative tool to downscale climate
variables from large-scale atmospheric fields under the perfect prognosis (PP) approach …

Abstract The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of
downscaled climate projections over Spain to feed the Second National Plan of Adaptation …

The aim of this work is to present preliminary results of the statistical and dynamical
simulations carried out within the framework of the Flagship Pilot Study in southeastern …

In a recent paper, Baño-Medina et al.(Configuration and Intercomparison of deep learning
neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep …