Several sets of reference regions have been used in the literature for the regional synthesis of observed
and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset.
We present 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 atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020)., This research has been supported by the Spanish National Plan for Scientific and Technical Research and Innovation (project PID2019-111481RB-I00 and María de Maeztu excellence programme projects MdM-2017-0765 and MdM-2017-0714), FCT MCTES financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020), and the Basque Government BERC 2018–2021 programme., Peer reviewed

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 convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of “black-box” models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs., The authors acknowledge partial support from the ATLAS project, funded by the Spanish Research Program (PID2019-111481RB-I00). We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu)., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature., Peer reviewed

The assessment of climate change impacts in regions with complex orography and land-sea interfaces poses a challenge related to shortcomings of global climate models. Furthermore, climate indices based on absolute thresholds are especially sensitive to systematic model biases. Here we assess the effect of bias adjustment (BA) on the projected changes in temperature extremes focusing on the number of annual days with maximum temperature above 35°C. To this aim, we use three BA methods of increasing complexity (from simple scaling to empirical quantile mapping) and present a global analysis of raw and BA CMIP5 projections under different global warming levels. The main conclusions are (1) BA amplifies the magnitude of the climate change signal (in some regions by a factor 2 or more) achieving a more plausible representation of future heat threshold-based indices; (2) simple BA methods provide similar results to more complex ones, thus supporting the use of simple and parsimonious BA methods in these studies., Joaquín Bedia, Ana Casanueva and Sixto Herrera acknowledge funding from the Project INDECIS, part of European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the European Union Grant 690462. José Manuel Gutiérrez and Josipa Milovac acknowledge the support of the Spanish Government through the Agencia Estatal de Investigación (project PID2019-111481RB-I00 and “Unidad de excelencia María de Maeztu” MdM-2017-0765). Maialen Iturbide acknowledges support from Universidad de Cantabria and Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria via the “instrumentación y ciencia de datos para sondear la naturaleza del universo” project., Peer reviewed

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR principles in the Atlas, the challenges faced during its implementation, and those that remain for the future. We introduce the open source repository resulting from this process, including coding (e.g., annotated Jupyter notebooks), data provenance, and some aggregated datasets used in some figures in the Atlas chapter and its interactive companion (the Interactive Atlas), open to scrutiny by the scientific community and the general public. We describe the informal pilot review conducted on this repository to gather recommendations that led to significant improvements. Finally, a working example illustrates the re-use of the repository resources to produce customized regional information, extending the Interactive Atlas products and running the code interactively in a web browser using Jupyter notebooks., We acknowledge partial funding from projects ATLAS (PID2019-111481RB-I00) funded by MCIN/AEI/10.13039/501100011033 and IS-ENES3 which is funded by the European Union’s H2020 programme under grant agreement No 824084. We also acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling and Working Group on Regional Climate, responsible for CMIP and CORDEX, respectively. We also thank the climate modeling groups for producing and making available their model output, as described in the data-source folder of the repository. We also acknowledge the Earth System Grid Federation infrastructure, an international effort led by the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP). The opinions expressed are those of the author(s) only and should not be considered as representative of the European Commission’s official position. JF and ASC acknowledge support from the CORDyS project (PID2020-116595RB-I00) funded by MCIN/AEI/10.13039/501100011033. JM acknowledges support from MDM-2017-0765 funded by MCIN/AEI/10.13039/501100011033. JBM acknowledges support from Universidad de Cantabria and Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria via the project “instrumentación y ciencia de datos para sondear la naturaleza del universo”., Peer reviewed

et al., The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS)., This work has been funded by the Copernicus Climate Change Service, implemented by ECMWF on behalf of the European Commission. JF and ASC acknowledge project CORDyS (PID2020-116595RB-I00) funded by MCIN/AEI/10.13039/501100011033. JMG and MI acknowledge project ATLAS (PID2019-111481RB-I00) funded by MCIN/AEI/10.13039/501100011033. ASC and EC acknowledge Project IS-ENES3 which is funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement 824084. JM acknowledges the support from the Spanish Agencia Estatal de Investigación through the Unidad de Excelencia María de Maeztu with Reference MDM-2017-0765. MED acknowledges the Partnership for advanced computing in Europe (PRACE) for awarding access to Piz Daint at ETH Zürich/Swiss National Supercomputing Centre (Switzerland), as well as the Federal Office for Meteorology and Climatology MeteoSwiss, the Swiss National Supercomputing Centre (CSCS), and ETH Zürich for their contributions to the development of the GPU-accelerated version of COSMO, COSMO-crCLIM., Peer reviewed

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

The performance of 61 global climate models participating in CMIP5 and 6 is evaluated for the Southern Hemisphere extratropics in terms of typical regional-scale atmospheric circulation patterns. These patterns are known to be linked with a number of key variables in atmospheric physics and chemistry and provide an overarching concept for model evaluation. First, hemispheric-wide error and ranking maps are provided for each model and regional details are described. Then, the results are compared with those obtained in a companion study for the Northern Hemisphere. For most models, the average error magnitude and ranking position is similar on both hemispheres, ruling out systematic tuning toward either of the two. CMIP6 models perform better on average than CMIP5 models and the interactive simulation of more climate system components does not deteriorate the results for most model families. Better performance is associated with higher resolution in the atmosphere, following a non-linear relationship., [Plain Language Summary] This letter provides a survey on the capability of global climate models to reproduce the regional atmospheric circulation in the Southern Hemisphere in present climate conditions. Climate models from the latest model generation are found to perform better on average than those of the previous generation and the obtained model ranking is similar to that found for the Northern Hemisphere in a companion study. While model performance is found to be generally unrelated to model complexity in terms of covered climate system components, better results are associated with higher model resolution in the atmosphere., This research work was funded by the European Commission – NextGenerationEU (Regulation EU 2020/2094), through CSIC's Interdisciplinary Thematic Platform Clima (PTI Clima)/Development of Operational Climate Services and by the I+D+i project CORDyS (PID2020-116595RB-I00), funded by MCIN/AEI/10.13039/501100011033. J.A.F. acknowledges support from project ATLAS (PID2019-111481RB-I00), grant PRE2020-094728 funded by MCIN/AEI/10.13039/501100011033 and ESF investing in your future. A.C. acknowledges support from Project COMPOUND (TED2021-131334A-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. S.B. would like to thank CESGA and AMTEGA for providing computational resources., Peer reviewed

The Jenkinson–Collison weather typing scheme (JC-WT) is an automated method used to classify regional sea-level pressure into a reduced number of typical recurrent patterns. Originally developed for the British Isles in the early 1970’s on the basis of expert knowledge, the method since then has seen many applications. Encouraged by the premise that the JC-WT approach can in principle be applied to any mid-to-high latitude region, the present study explores its global extra-tropical applicability, including the Southern Hemisphere. To this aim, JC-WT is applied at each grid-box of a global 2.5
regular grid excluding the inner tropics (± 5º band). Thereby, 6-hourly JC-WT catalogues are obtained for 5 distinct reanalyses, covering the period 1979–2005, which are then applied to explore (1) the limits of method applicability and (2) observational uncertainties inherent to the reanalysis datasets. Using evaluation criteria, such as the diversity of occurring circulation types and the frequency of unclassified situations, we extract empirically derived applicability thresholds which suggest that JC-WT can be generally used anywhere polewards of 23.5º, with some exceptions. Seasonal fluctuations compromise this finding along the equatorward limits of the domain. Furthermore, unreliable reanalysis sea-level pressure estimates in elevated areas with complex orography (such as the Tibetan Plateau, the Andes, Greenland and Antarctica) prevent the application of the method in these regions. In some other regions, the JC-WT classifications obtained from the distinct reanalyses substantially differ from each other, which may bring additional uncertainties when the method is used in model evaluation experiments., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature., This paper is part of the R+D+i projects CORDyS (PID2020-116595RB-I00) and ATLAS (PID2019-111481RB-I00), funded by MCIN/AEI/10.13039/501100011033. J.A.F. has received research support from grant PRE2020-094728 funded by MCIN/AEI/10.13039/501100011033. J.B. and A.C. received research support from the project INDECIS, part of the European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the European Union (grant no. 690462)., Peer reviewed

Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional-to-local scales from large-scale atmospheric fields following the perfect-prognosis approach. Given their complexity, it is crucial to properly evaluate these methods, especially when applied to changing climatic conditions where the ability to extrapolate/generalize is key. In this work, we intercompare several DL models extracted from the literature for the same challenging use-case (downscaling temperature in the CORDEX North America domain) and expand standard evaluation methods building on eXplainable Artificial Intelligence (XAI) techniques. Specifically, we introduce two novel XAI-based diagnostics—Aggregated Saliency Map and Saliency Dispersion Maps—and show how they can be used to unravel the internal behavior of these models, aiding in their design and evaluation. This work advocates for the introduction of XAI techniques into deep downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions., We acknowledge partial funding from projects ATLAS (PID2019-111481RB-I00) funded by MCIN/AEI 10.13039/501100011033. J. González-Abad would like to acknowl-edge the support of the funding from the Spanish Agencia Estatal de Investigación through the Unidad de Excelencia María de Maeztu with reference MDM-2017-0765. Also, J. Baño-Medina acknowledges support from Universidad de Cantabria and Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria via the “instrumentación y ciencia de datos para sondear la naturaleza del universo” project, Peer reviewed

Assessing the regional responses to different Global Warming Levels (GWLs; e.g. + 1.5, 2, 3 and 4 ºC) is one of the most important challenges in climate change sciences since the Paris Agreement goal of keeping global temperature increase well below 2 °C with respect to the pre-industrial period. Regional responses to global warming were typically analyzed using global projections from Global Climate Models (GCMs) and, more recently, using higher resolution Regional Climate Models (RCMs) over limited regions. For instance, the IPCC AR6 WGI Atlas provides results of the regional response to different GWLs for several climate variables from both GCMs and RCMs. These results are calculated under the assumption that the regional signal to global warming is consistent between the GCMs and the nested RCMs. In the present study we investigate the above assumption by evaluating the consistency of regional responses to global warming from global (CMIP5) and regional (CORDEX) projections. The dataset aggregated over the new IPCC reference regions, available from the IPCC AR6 WGI Atlas repository, is analyzed here for temperature and precipitation. The existing relationships between the regional climate change signals and global warming are compared for both CMIP5 and CORDEX. Our results show significant linear scaling relationships between regional changes and global warming for most of the regions. CORDEX and CMIP5 show remarkably similar scaling relationships and similar robustness in the emergence of the climate change signal for most of the regions. These results support the use of regional climate models in the context of global warming level studies., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature., This work is part of project ATLAS (PID2019-111481RB-I00) funded by MCIN/AEI/10.13039/501100011033. JF and ASC acknowledge support from project CORDyS (PID2020-116595RB-I00) funded by MCIN/AEI/10.13039/501100011033. ASC acknowledges project IS-ENES3 which is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824084., Peer reviewed

Global Climate Models (GCMs) depict a notable influence of climate change on southern South America (SSA). Future regional-to-local information for adaptation and mitigation policies can be obtained by downscaling over GCMs outputs, increasing the resolution of the climate projections. Current statistical downscaling approaches in the region [e.g., Generalised Linear Models (GLMs)] need to undergo “human-guided” feature selection, which is one of the main sources of uncertainty. Here, we explore the advantages and limitations of using Convolutional Neural Networks (CNNs) in SSA to downscale daily minimum and maximum temperatures. For this purpose, we elaborate three different experiments: a cross-validation (CV) in the present climate; downscaling the historical and RCP8.5 scenarios of the EC-Earth; a pseudo-reality experiment to measure the extrapolation skill. CV-experiment results show no remarkable differences between CNNs and GLMs, although the non-linearity of the CNNs improved the representation of the extreme aspects of temperatures. Additionally, we use eXplainable Artificial Intelligence to prove that co-linearities are better handled in CNNs. The pseudo-reality experiment shows a good extrapolation skill of CNNs, especially when the activation functions are linear. Overall, the automatic skill of CNNs to deal with co-linearities in predictor data—against conventional approaches—together with the plausible climate change projections obtained—verified with the pseudo-reality experiment—make them attractive to be used for downscaling beyond their non-linear nature. These results enforce the idea of incorporating CNNs into the battery of downscaling techniques over SSA and provide experimental guidelines with prospects to be utilised in climate change studies., R. Balmaceda-Huarte has received a mobility grant from “AUIP Programa de Becas de Movilidad Académica 2021” that made this collaboration possible. Also, J. Baño-Medina acknowledges support from Universidad de Cantabria and Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria via the “instrumentación y ciencia de datos para sondear la naturaleza del universo'' project. This work was supported by the Argentinian projects 2018-20020170100117BA, 20020170100357BA from the University of Buenos Aires and the ANPCyT PICT-2018-02496 and PICT-2019-02933. The authors acknowledge partial support from the ATLAS project from the Spanish Research Program (AEI; PID2019-111481RB-I00)., Peer reviewed

A. Belušić Vozila et al., We present the first evaluation of the wind field from the ensemble of kilometer-scale simulations from the CORDEX-Flagship Pilot Study on convection, with focus on the Adriatic region. Kilometer-scale climate models, also known as convection-permitting models (CPMs), produce a good representation of small-scale topographic features and consequently a more detailed depiction of dynamical and thermal circulations. These enable a reliable view of climate characteristics of the wind field, especially in coastal regions and over complex terrain, such as the Adriatic region. We investigate the (potential) added value introduced by CPMs compared to classical “cumulus-parametrized” regional climate models (RCMs), reanalysis and station observations. For this purpose, wind components at 10 m level are used at 3-hourly frequency. All simulations cover a 10-year period, extending from 2000 to 2009. In terms of the standard statistical parameters such as correlation coefficient and temporal standard deviation, CPMs are very dependent on their parent RCM performance. However, the orographic forcing emphasizes the potential added value and CPMs contain some fine spatial scale variability (i.e., stronger extremes by 25% and more accurate wind direction) that is absent in coarser RCMs and reanalysis. The potential added value is higher in the cold season compared to the warm season due to the proportion of severe wind events. CPMs reproduce well the typical wind regimes along the Adriatic coast, namely Bora and Sirocco. The benefit of using CPMs is especially pronounced in simulating Bora maximum wind speeds in northern Adriatic and Sirocco frequencies in southern Adriatic. Based on our overall analysis, we conclude that CPMs provide added value compared to coarser models, especially in the complex coastal terrain., The ETH, IPSL, ICTP, SMHI, CMCC, KNMI acknowledge funding from the HORIZON 2020 EUCP (European Climate Prediction System) project (https:// www. eucp- project.eu, grant agreement No. 776613). DB acknowledges funding from the FORMAS project EDUCAS (grant no. 2019-00829). ØH has received support from the project GREAT, funded by the Research Council of Norway (grant no. 275589), and acknowledge computing resources from Notur (NN9188K). JM acknowledges the support from the Spanish Agencia Estatal de Investigación through the Unidad de Excelencia María de Maeztu with reference MDM-2017-0765, the projects CORDyS (PID2020-116595RB-I00) and ATLAS (PID2019-111481RB-I00), both funded by MCIN/AEI/10.13039/501100011033. Funded by the Austrian Education, Science and Research Ministry (BMBWF), as well as for the support received via the project “reclip:convex”, funded by the Austrian Climate Research Programme (ACRP) of the Klima- und Energiefonds (no. B769999)., Peer reviewed

M. T. Ha et al., Recent studies using convection-permitting (CP) climate simulations have demonstrated a step-change in the representation of heavy rainfall and rainfall characteristics (frequency-intensity) compared to coarser resolution Global and Regional climate models. The goal of this study is to better understand what explains the weaker frequency of precipitation in the CP ensemble by assessing the triggering process of precipitation in the different ensembles of regional climate simulations available over Europe. We focus on the statistical relationship between tropospheric temperature, humidity and precipitation to understand how the frequency of precipitation over Europe and the Mediterranean is impacted by model resolution and the representation of convection (parameterized vs. explicit). We employ a multi-model data-set with three different resolutions (0.44°, 0.11° and 0.0275°) produced in the context of the MED-CORDEX, EURO-CORDEX and the CORDEX Flagship Pilot Study "Convective Phenomena over Europe and the Mediterranean" (FPSCONV). The multi-variate approach is applied to all model ensembles, and to several surface stations where the integrated water vapor (IWV) is derived from Global Positioning System (GPS) measurements. The results show that all model ensembles capture the temperature dependence of the critical value of IWV (IWVcv), above which an increase in precipitation frequency occurs, but the differences between the models in terms of the value of IWVcv, and the probability of its being exceeded, can be large at higher temperatures. The lower frequency of precipitation in convection-permitting simulations is not only explained by higher temperatures but also by a higher IWVcv necessary to trigger precipitation at similar temperatures, and a lower probability to exceed this critical value. The spread between models in simulating IWVcv and the probability of exceeding IWVcv is reduced over land in the ensemble of models with explicit convection, especially at high temperatures, when the convective fraction of total precipitation becomes more important and the influence of the representation of entrainment in models thus becomes more important. Over lowlands, both model resolution and convection representation affect precipitation triggering while over mountainous areas, resolution has the highest impact due to orography-induced triggering processes. Over the sea, since lifting is produced by large-scale convergence, the probability to exceed IWVcv does not depend on temperature, and the model resolution does not have a clear impact on the results., To process the data, this study benefited from the IPSL mesocenter ESPRI facility which is supported by CNRS, UPMC, Labex L-IPSL, CNES and Ecole Polytechnique, and received funding from the HORIZON 2020 EUCP (European Climate Prediction System) project (https://www.eucp-project.eu, grant agreement No. 776613). IPSL, CNRM and SMHI also acknowledge funding from the HORIZON 2020 EUCP. IPSL’s simulation was granted access to the HPC resources of IDRIS under the allocations of the project 0227 " Fonctionnement de la Mediterranee : circulation et ecosystemes" and HPC resources of TGCC under the allocation A0090106877 made by GENCI. The GPS and SIRTA-ReOBS datasets are maintained by the French national center for Atmospheric data and services AERIS and Research Infrastructure ACTRIS-FR. J.M. acknowledges the support of the Spanish Government through the Agencia Estatal de Investigacion (project PID2019-111481RB-I00 and ``Unidad de excelencia Maria de Maeztu'' MdM-2017-0765). Ø.H. has received support from the project GREAT, funded by the Research Council of Norway (grant no. 275589), and acknowledge computing resources from Notur (NN9188K). AUTH simulations were supported by computational time granted from the National Infrastructures for Research and Technology S.A. (GRNET S.A.) in the National HPC facility—ARIS—under project pr009020_thin. UCAN simulations have been carried out on the Altamira Supercomputer at the Instituto de Física de Cantabria (IFCA-CSIC), member of the Spanish Supercomputing Network. JF acknowledges support from project CORDyS (PID2020-116595RB-I00) funded by MCIN/AEI/10.13039/501100011033. K.G. and H.T. acknowledge the computing time granted by the John von Neumann Institute for Computing (NIC) and through JARA-HPC on the supercomputer JURECA at the Jülich Supercomputing Centre (JSC) through the grant JJSC39. H.T. additionally acknowledges the support received via the project “reclip:convex”, funded by the Austrian Climate Research Programme (ACRP) of the Klima- und Energiefonds (no. B769999), the Vienna Scientific Cluster (VSC) via the grants 70992 and 71193, as well as the cooperation project GEOCLIM Data Infrastructure Austria, funded by the Austrian Education, Science and Research Ministry (BMBWF)., Peer reviewed

Sea surface temperature (SST) and sea surface air temperature (SSAT) are commonly used as proxies for investigating the impact of climate change on oceans. These variables have been warming since pre-industrial times and are expected to continue to warm in the future under all Shared Socioeconomic Pathways (SSPs). However, they are warming in a spatially heterogeneous way, even with some cooling spots. In this work, we provide a general overview on the regional scaling of SST and SSAT with global warming, based on a 26-member CMIP6 ensemble. We utilize the global warming level (GWL) as a climate change dimension to analyze scaling patterns between sea temperature anomalies and the corresponding GWLs during the 21st century. This analysis is conducted globally, regionally, and on grid-point basis. The results show that SST and SSAT scale linearly with GWL at global scale, with scaling factors β =0.71± 0.001 K/K and β =0.86± 0.001 K/K, respectively. These results are robust, showing only minor differences between seasons, SSPs, and horizontal model resolutions. However, large differences emerge at regional scale, and the scaling of the two temperatures are strongly influenced by sea-ice. The lowest values are obtained for the Southern Ocean region, β =0.54± 0.005 K/K, projecting that the mean SST will increase only half as fast as the global mean temperature. These results provide valuable insight for refining the ocean IPCC reference regions, considering spatial homogeneity in terms of the regional response to global warming. A refinement of six ocean reference regions has been proposed., This work is part of the project ATLAS (PID2019-111481RB-I00) funded by MCIN/AEI/10.13039/501100011033. JF and JM acknowledge support from the project CORDyS (PID2020-116595RB-I00) funded by MCIN/AEI/10.13039/501100011033. RGJ acknowledges support from the Met Office Hadley Centre Climate Programme funded by the UK government’s Department for Science, Innovation and Technology., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature., Peer reviewed

The Jenkinson-Collison Weather Typing (JC-WT) method uses sea-level pressure gradients to create 27 types based on the geostrophic flow and vorticity around any extratropical target location. Typically, JC-WTs are applied over specific locations or limited domains, thus hampering the understanding of the impact of large-scale mechanisms on regional climate. This study explores the links between regional climate variability, as represented by the JC-WTs, and large-scale phenomena, to describe the synoptic-scale variability in the North Atlantic-European region and evaluate the JC-WT methodology. Large-scale circulation is here characterized by major atmospheric low-frequency modes, namely the North Atlantic Oscillation, the East Atlantic and the Scandinavian teleconnection indices, and by atmospheric blockings. Results show that JC-WTs coherently capture the spatial and temporal variability of the large-scale modes and yields a characteristic response to blocking events. Overall, our results underpin the exploratory potential of this method for the analysis of the near-surface circulation. These findings endorse the use of JC-WTs and support the reliability and utility of the JC-WT classification for process-based model assessments and model selection, a crucial task for climate impact studies., We acknowledge support from projects CORDyS (PID2020-116595RB-I00) and ATLAS (PID2019-111481RB-I00). J.A.F.-G. acknowledges support from grant PRE2020-094728 funded by MCIN/AEI/10.13039/501100011033 and “ESF: Investing in Your Future.” A.C. acknowledges support from Project COMPOUND (TED2021-131334A-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. J.F. acknowledges support from the European Union's Horizon Europe research and innovation programme under grant agreement no. 101081555 (IMPETUS4CHANGE). S.B. was funded by the Ministry for the Ecological Transition and the Demographic Challenge (MITECO) and the European Commission NextGenerationEU (Regulation EU 2020/2094), through CSIC's Interdisciplinary Thematic Platform Clima (PTI-Clima)., Peer reviewed

Data Availability Statement: In this study, no new data have been generated. PTI-Clima v0 and HUMID01 are currently not disclosed to the public; STEAD and Iberia01 are publicly available through the Digital.CSIC repository. Iberia01 is made available under the Open Database Licence; the ROCIO-IBEB dataset is freely distributed for use in research work, and commercial use is not permitted (for information on the commercial use of this grid you must contact AEMET). The work and products carried out, in whole or in part, with this data must also be public and must be made available to the scientific community free of charge. Likewise, to guarantee access to the updated original source, redistribution of data to third parties is not permitted; the EMO-1arcmin dataset is provided under the licence Creative Commons Attribution 4.0 International; CHELSA-W5E5 is publicly available through the ISIMIP Repository under the CC0 1.0 Universal Public Domain Dedication licence; E-OBS v27e, ECMWF-ERA5-Land and CERRA-SFC datasets are publicly available from the Copernicus Climate Data Store under the terms of use defined for the Copernicus Products., The sensitivity to the observational reference has been reported in recent studies, highlighting the importance of observational uncertainty in climate research. These studies stress the importance of properly comparing available datasets, recognising their respective strengths and limitations. Here, we conduct a comprehensive analysis of the various datasets of maximum and minimum daily temperatures available for mainland Spain. We examined 10 publicly available daily gridded datasets of maximum and minimum temperatures, analysing multiple evaluation dimensions to identify the key strengths and limitations of each dataset: statistical distribution, extreme events, temporal structure and spells and spatial patterns. We conclude that observational uncertainty is greater for minimum temperatures than for maximum temperatures. This uncertainty is not strictly linked to the type of dataset (interpolation, analysis or reanalysis) or its spatial domain (national, European or global) but rather to specific datasets which vary depending on the analysis dimension. Overall, the most stable dataset across all evaluated indices is STEAD, whereas the PTI-Clima v0 dataset exhibits some underestimation of extremes and spells but performs well in capturing central parameters and temporal correlations., This study was supported by the MCIN/AEI (RYC2021-034330-I), Agencia Estatal de Investigación (PID2019-111481RB-I00) and European Commission – NextGenerationEU., Peer reviewed

Data availability statement: All the data necessary to reproduce the experiments in this study are publicly available. ERA5 data can be downloaded from the C3S Climate Data Store after registration and acceptance of the licensing terms (https://doi.org/10.24381/cds.50314f4c). Data from EC-Earth3-Veg, MPI-ESM1-2-LR, and CMCC-CM2-SR5 can be accessed through the Earth System Grid Federation (ESGF) portal (https://esgf-data.dkrz.de) by selecting the CMIP6 project and the CMIP activity. The ROCIO-IBEB dataset is available on the AEMET website (https://www.aemet.es/en/serviciosclimaticos/cambio_climat/datos_diarios?w=2); however, please note that this web page is only available in Spanish. To download minimum and maximum temperature data, locate the tar.gz files under section 2, Rejillas ROCIO_IBEB de temperaturas diarias extremas. For precipitation data, refer to section 1, Rejilla ROCIO_IBEB de precipitación. The code used to reproduce these experiments is publicly accessible at https://doi.org/10.5281/zenodo.14016954., Deep learning (DL) has shown promise for downscaling global climate change projections under different approaches, including perfect prognosis (PP) and regional climate model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data, so it remains an open question whether they can plausibly extrapolate unseen conditions and changes in future emissions scenarios. Here, we focus on this problem as the main drawback for the operationalization of these methods and present the results of an intercomparison experiment to evaluate the performance and extrapolation capability of existing models using a common experimental framework, taking into account the sensitivity of results to different training replicas. We focus on minimum and maximum temperatures and precipitation over Spain, a region with a range of climatic conditions with different influential regional processes. We conclude with a discussion of the findings, limitations of existing methods, and prospects for future development., [Significance Statement] Deep learning techniques have recently emerged as a promising approach to enhance the spatial resolution of coarse climate models, a process known as statistical downscaling. In this study, we review current methods and evaluate two popular models, ensemble of convolutional neural network (CNN)-based downscaled projections (DeepESD) and U-Net, to assess their ability to project future climate changes over Spain, a region characterized by diverse and complex climates. While both models perform well with historical data, their effectiveness in projecting future periods varies. DeepESD demonstrates particular promise when tailored to focus on extreme events, though challenges persist in accurately modeling such events under changing climate conditions. This work provides guidance for advancing climate downscaling with deep learning, identifying the key areas for further research., This work was partially supported by the project ATLAS (PID2019-717 111481RB-I00) funded by MCIN/AEI/10.13039/501100011033. González-Abad acknowledges support from Grant CPP2021-008510 funded by MICIU/AEI/10.13039/501100011033 and by the “European Union” and the “European Union NextGenerationEU/PRTR,” as well as from Project COMPOUND (TED2021-131334A-I00) funded by MCIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR., Peer reviewed

This study examines how large-scale near-surface circulation is projected to evolve under anthropogenic forcing throughout the 21st century. Using a multi-model ensemble of 41 state-of-the-art Global Climate Models high emissions scenarios, we analyze changes in Weather Type (WT) frequencies, based on Sea-Level Pressure (SLP), as a function of global warming and estimate their Time of Emergence (ToE) from historical variability. Significant and robust trends are identified across key climatic regions, including shifts in anticyclonic types in subtropical high-pressure belts and the Mediterranean, westerly and cyclonic types in the Subantarctic, and unclassified types in the Mediterranean. While most robust signals emerge by the late 21st century, some changes in the Mediterranean are already emerging in the present decade. Overall, results reveal significant circulation changes in the coming decades, affecting dominant near-surface circulation modes globally. A comprehensive dataset of 6-hourly WT projections (2006/15-2100) over mid-latitudes is publicly available as part of this study., This study is part of the R+D+i projects CORDyS (PID2020-116595RB-I00) and ATLAS (PID2019-111481RB-I00), funded by MCIN/AEI/10.13039/501100011033. J.A.F. acknowledges support from grant PRE2020-094728 funded by MCIN/AEI/10.13039/501100011033. A.C. and J.B. acknowledge support from Project PID2023-149997OA-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. S.B. was funded by the PROYECTOS DE GENERACIÓN DE CONOCIMIENTO CONTRIBUCION ESPAÑOLA AL ATLAS DEL IPCC-AR7: DESARROLLO Y PROBLEMAS CIENTIFICOS (PID2024-162703OB-I00). J.F. acknowledges support from the European Union’s Horizon Europe research and innovation programme under grant agreement Nº 101081555 (IMPETUS4CHANGE)., Peer reviewed

The sensitivity to the observational reference has been reported in recent studies, highlighting the importance of observational uncertainty in climate research. These studies stress the importance of properly comparing available datasets, recognising their respective strengths and limitations. Here, we conduct a comprehensive analysis of the various datasets of maximum and minimum daily temperatures available for mainland Spain. We examined 10 publicly available daily gridded datasets of maximum and minimum temperatures, analysing multiple evaluation dimensions to identify the key strengths and limitations of each dataset: statistical distribution, extreme events, temporal structure and spells and spatial patterns. We conclude that observational uncertainty is greater for minimum temperatures than for maximum temperatures. This uncertainty is not strictly linked to the type of dataset (interpolation, analysis or reanalysis) or its spatial domain (national, European or global) but rather to specific datasets which vary depending on the analysis dimension. Overall, the most stable dataset across all evaluated indices is STEAD, whereas the PTI-Clima v0 dataset exhibits some underestimation of extremes and spells but performs well in capturing central parameters and temporal correlations.