Deep learning (DL) methods have recently garnered attention from the climate change community for being an innovative approach to downscaling climate variables from Earth system and global climate models (ESGCMs) with horizontal resolutions still too coarse to represent regional- to local-scale phenomena. In the context of the Coupled Model Intercomparison Project phase 6 (CMIP6), ESGCM simulations were conducted for the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) at resolutions ranging from 0.70 to 3.75^∘. Here, four convolutional neural network (CNN) architectures were evaluated for their ability to downscale, to a resolution of 0.1^∘, seven CMIP6 ESGCMs over the Iberian Peninsula – a known climate change hotspot, due to its increased vulnerability to projected future warming and drying conditions. The study is divided into three stages: (1) evaluating the performance of the four CNN architectures in predicting mean, minimum, and maximum temperatures, as well as daily precipitation, trained using ERA5 data and compared with the Iberia01 observational dataset; (2) downscaling the CMIP6 ESGCMs using the trained CNN architectures and further evaluating the ensemble against Iberia01; and (3) constructing a multi-model ensemble of CNN-based downscaled projections for temperature and precipitation over the Iberian Peninsula at 0.1^∘ resolution throughout the 21st century under four Shared Socioeconomic Pathway (SSP) scenarios. Upon validation and satisfactory performance evaluation, the DL downscaled projections demonstrate overall agreement with the CMIP6 ESGCM ensemble in magnitude for temperature projections and sign for the projected temperature and precipitation changes. Moreover, the advantages of using a high-resolution DL downscaled ensemble of ESGCM climate projections are evident, offering substantial added value in representing regional climate change over Iberia. Notably, a clear warming trend is observed in Iberia, consistent with previous studies in this area, with projected temperature increases ranging from 2 to 6 ^∘C, depending on the climate scenario. Regarding precipitation, robust projected decreases are observed in western and southwestern Iberia, particularly after 2040. These results may offer a new tool for providing regional climate change information for adaptation strategies based on CMIP6 ESGCMs prior to the next phase of the European branch of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) experiments.

A quantile-based bias-correction method is applied to a seven-member dynamic ensemble of global wave climate simulations with the aim of reducing the significant wave heightH_S, mean wave periodT_m, and mean wave direction (MWD) biases, in comparison with the ERA5 reanalysis. The corresponding projected changes toward the end of the twenty-first century are assessed. Seven CMIP5 EC-EARTH runs (single forcing) were used to force seven wave model (WAM) realizations (single model), following the RCP8.5 scenario (single scenario). The biases for the 1979–2005 reference period (present climate) are corrected using the empirical Gumbel quantile mapping and empirical quantile mapping methods. The same bias-correction parameters are applied to theH_S,T_m(and wave energy fluxP_w), and MWD future climate projections for the 2081–2100 period. The bias-corrected projected changes show increases in the annual meanH_S(14%),T_m(6.5%), andP_w(30%) in the Southern Hemisphere and decreases in the Northern Hemisphere (mainly in the North Atlantic Ocean) that are more pronounced during local winter. For the upper quantiles, the bias-corrected projected changes are more striking during local summer, up to 120%, forP_w. After bias correction, the magnitude of theH_S,T_m, andP_woriginal projected changes has generally increased. These results, albeit consistent with recent studies, show the relevance of a quantile-based bias-correction method in the estimation of the future projected changes in swave climate that is able to deal with the misrepresentation of extreme phenomena, especially along the tropical and subtropical latitudes.

The energy content of wind-waves is propagated across the oceans in the form of swell waves, the main drivers of long-term changes in coastal morphology and offshore hazards. A state-of-the-art swell tracking algorithm is applied to a global ensemble of CMIP5 dynamic wave climate projections, to assess future changes in remotely originated swell events towards the end of the 21st century, and how they propagate. The contribution of multiple wave generation areas is considered. It is found that the projected climate change signal is effectively propagated from the winds along the extratropical storm tracks to remote locations, in the tropical and subtropical latitudes, through swell waves. The statistically significant projected changes in swell wave heights and swell predominance at the remote swell arrival locations are comparable with the ones at the wave generation areas. Furthermore, different incoming directions for swell events at remote locations are shown to often carry opposite climate change signals, propagated from different remote origins. These results highlight the need for a directional approach on wave climate projections, critical for improved vulnerability assessments and adaptation measures from the climate community.