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Drought is a complex climatic phenomenon characterized by water scarcity recognized as the most widespread and insidious natural hazard, posing significant challenges to ecosystems and human society. In this study, we propose a new daily based index for characterizing droughts, which involves standardizing precipitation and/or precipitation minus potential evapotranspiration data. The performance of this new index is assessed with data from the evaluation runs of the Coordinated Regional Climate Downscaling Experiment for the European domain and the observational data from the Iberian Gridded Dataset, covering the period from 1989 to 2009. Comparative assessments are conducted against the daily Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and a simple Z-Score standardization of climatic variables. Seven different accumulation periods are considered (7, 15, 30, 90, 180, 360, and 720 days) with three drought levels: moderate, severe, and extreme. The evaluation focuses mainly on the direct comparison amongst indices, added value assessment using the Distribution Added Value and a simple bias difference for drought characteristics. Results reveal that not only does the new index allow the characterization of flash droughts, but also demonstrates added value when compared to SPI and SPEI, especially for longer accumulation periods. In comparison to the Z-Score, the new index shows slightly greater gains, particularly for extreme drought events at lower accumulation periods. Furthermore, an assessment of the spatial extent of drought for the 2004–2005 event is performed using the observational dataset. All three indices generally provide similar representations, except for the Z-Score, which exhibits limitations in capturing extreme drought events at lower accumulation periods. Overall, the findings suggest that the new index offers improved performance and adds value comparatively to similar indices with a daily time step. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Deep learning (DL) methods have recently garnered attention from the climate change community, as an innovative approach for 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), ESGCMs 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 CNNbased 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 terms of temperature and precipitation projections. 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, consistent with previous studies in this area, with projected temperature increases ranging from 2°C 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 from the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) experiments. [ABSTRACT FROM AUTHOR]

Regional climate simulations with high horizontal resolutions are becoming increasingly common and although model development has continually enhanced the representation of atmospheric phenomena, the model improvements are variable, region and time scale‐dependant. The high computational costs of increasingly smaller grid‐spacing underline the need for a robust assessment of the benefits or losses associated to the dynamical downscaling of coarser resolution models (reanalysis, global climate models or ~tenths km runs), that is, quantitative added value evaluation. In the current study, a probability density function (PDF) matching score is used to determine the distribution added value (DAV) of the EURO‐CORDEX maximum and minimum temperatures from the hindcast simulations at 0.44° and 0.11° resolutions. The gridded maximum and minimum temperatures from European Climate Assessment & Dataset (E‐Obs) were used as benchmarks for the matching scores and DAVs were determined for the 0.44° and 0.11° simulations individually against ERA‐Interim as well as against each other. Temperature added value against ERA‐Interim is difficult to find due to the assimilation of surface temperatures in the reanalysis. Nonetheless, there is positive added value in Tmax for half of the models at the yearly time scale, and the increase in resolution also implies positive DAV in half of the models, with DAVs between 0.1 and 3.8%. More importantly, the benefits of downscaling are significantly visible in the extreme end tail of Tmax, where all models have high added value at all resolutions and seasons except in summer (from ~1 to ~21%). In minimum temperature there is added value in winter and autumn at both resolutions. The Mediterranean and the British Isles are the regions where larger positive DAV values for both variables and resolutions are identified. In Tmax, added value is also associated to the increase in resolution (from 50 to 10 km) for half of the models in all regions. Scandinavia, the Alps, and eastern Europe are regions where negative added value is clearly linked with the highest mountain peaks for both variables. [ABSTRACT FROM AUTHOR]

In the recent past, an increase in computation resources led to the development of regional climate models with increasing domains and resolutions, spanning larger temporal periods. A good example is the World Climate Research Program – Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX). This set of regional models encompasses the entire European continent for a 130-year common period until the end of the 21st century, while having a 12 km horizontal resolution. Such simulations are computationally demanding, while at the same time not always showing added value. This study considers a recently proposed metric in order to assess the added value of the EURO-CORDEX hindcast (1989–2008) and historical (1971–2005) simulations for the maximum and minimum temperature over the Iberian Peninsula. This approach allows an evaluation of the higher against the driving lower resolutions relative to the performance of the whole or partial probability density functions by having an observational regular gridded dataset as a reference. Overall, the gains for maximum temperature are more relevant in comparison to minimum temperature, partially due to known problems derived from the snow–albedo–atmosphere feedback. For more local scales, areas near the coast reveal higher added value in comparison with the interior, which displays limited gains and sometimes notable detrimental effects with values around - 30 %. At the same time, the added value for temperature extremes reveals a similar range, although with larger gains in coastal regions and in locations from the interior for maximum temperature, contrasting with the losses for locations in the interior of the domain for the minimum temperature. [ABSTRACT FROM AUTHOR]

Over the years, higher-resolution regional climate model simulations have emerged owing to the large increase in computational resources. The 12 km resolution from the Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX) is a reference, which includes a larger multi-model ensemble at a continental scale while spanning at least a 130-year period. These simulations are computationally demanding but do not always reveal added value. In this study, a recently developed regular gridded dataset and a new metric for added value quantification, the distribution added value (DAV), are used to assess the precipitation of all available EURO-CORDEX hindcast (1989–2008) and historical (1971–2005) simulations. This approach enables a direct comparison between the higher-resolution regional model runs against their forcing global model or ERA-Interim reanalysis with respect to their probability density functions. This assessment is performed for the Iberian Peninsula. Overall, important gains are found for most cases, particularly in precipitation extremes. Most hindcast models reveal gains above 15 %, namely for wintertime, while for precipitation extremes values above 20 % are reached for the summer and autumn. As for the historical models, although most pairs display gains, regional models forced by two general circulation models (GCMs) reveal losses, sometimes around - 5 % or lower, for the entire year. However, the spatialization of the DAV is clear in terms of added value for precipitation, particularly for precipitation extremes with gains well above 100 %. [ABSTRACT FROM AUTHOR]

In the recent past, the increase of computation resources led to the appearance of regional climate models with increasing domains and resolutions, spamming larger temporal periods. A good example is the World Climate Research Program - Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX). This set of regional models encompass the entire European continent, for a 130-year common period until the end of the 21st century, while having a 12 Km horizontal resolution. Such simulations are computationally demanding, while at the same time, not always showing added value. This study considers a recently proposed metric in order to assess the added value of the EURO-CORDEX Hindcast (1989-2008) and Historical (1971-2005) simulations, for the maximum and minimum temperature over the Iberian Peninsula. This approach allows an evaluation of the higher against the driving lower resolutions relative to the performance of the whole or partial probability density functions, by having an observational regular gridded dataset as reference. Overall, the gains for maximum temperature are more relevant in comparison to minimum temperature, partially owed to known problems derived from the snow-albedo-atmosphere feedback. For more local scales, areas near the coast reveal significant added value in comparison with the interior, which displays limited gains and sometimes significant detrimental effects around −30 %. Nevertheless, the added value for temperature extremes reveals a similar range, although with stronger gains in coastal regions and in locations from the interior for maximum temperature, contrasting with the losses for locations in the interior of the domain for the minimum temperature. [ABSTRACT FROM AUTHOR]

Over the years higher resolution Regional Climate Model simulations have emerged owing to the large increase in computational resources. The 12 Km resolution from the Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX) is a reference, which includes a larger multi-model ensemble at a continental scale while spanning at least a 130-year period. These simulations are computationally demanding but not always revealing added value. In this study, a recently developed regular gridded dataset (Iberia0.1) and a new metric for added value quantification, the distribution added value (DAV), are used to assess the precipitation of all available EURO-CORDEX Hindcast (1989-2008) and Historical (1971-2005) simulations. This approach enables a direct assessment between the higher resolution regional model runs against their forcing Global model or ERA-Interim reanalysis, with respect to their PDFs. This assessment is performed for the Iberian Peninsula. Overall, important gains are found for most cases, particularly in precipitation extremes. Most Hindcast models reveal gains above 15 %, namely for wintertime, while for precipitation extremes values above 20 % are reached for the summer and autumn. As for the Historical models, although most pairs display gains, regional models forced by 2 GCMs reveal losses, sometimes around −5 % or stronger, for the entire year. However, the spatialization of the DAV is clear in terms of added value for precipitation, particularly precipitation extremes with significant gains, above 100 %. [ABSTRACT FROM AUTHOR]

Credible information about the properties and changes of extreme events on the regional and local scales is of prime importance in the context of future climate change. Within the EU‐COST Action VALUE a comprehensive validation framework for downscaling methods has been developed. Here we present validation results for extremes of temperature and precipitation from the perfect predictor experiment that uses reanalysis‐based predictors to isolate downscaling skill. The raw reanalysis output reveals that there is mostly a large bias with respect to the extreme index values at the considered stations across Europe, clearly pointing to the necessity of downscaling. The performance of the downscaling methods is closely linked to their specific structure and setup. All methods using parametric distributions require non‐standard distributions to correctly represent marginal aspects of extremes. Also, the performance is much improved by explicitly including a seasonal component, particularly in case of precipitation. With respect to the marginal aspects of extremes the best performance is found for model output statistics (MOS), weather generators (WGs) as well as perfect prognosis (PP) methods using analogues. Spell‐length‐related extremes of temperature are best assessed by MOS and WGs, spell‐length‐related extremes of precipitation by MOS and PP methods using analogues. The skill of PP methods with transfer functions varies strongly across the methods and depends on the extreme index, region and season considered. [ABSTRACT FROM AUTHOR]

Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short‐term variations, spells and variability from interannual to long‐term trends. The EU‐COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long‐term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors. [ABSTRACT FROM AUTHOR]

The spatial variability and the climatic characteristics of the coastal low-level jet along the west coast of the Iberian Peninsula are presented in this study, based on a 9-km resolution downscaling data set, produced using the weather research and forecasting model, forced by the ERA-Interim reanalysis. The west coast of Iberia is divided into two (north and south) subareas, where the Iberian Peninsula coastal jet (IPCJ) characteristics are studied separately, and compared to the whole western Iberia. It is shown that while IPCJ occurrences are more frequent in the southern area, during summer (June–August), the jet mean wind speed maxima is highest in the northernmost area. The along- and cross-coast sea surface temperature (SST) structure is studied and linked to the coastal wind speed. It is also shown that the local cross-coast marine atmospheric boundary layer gradient at the coast is considerably higher than the synoptic one, and that this difference is responsible for the local intensification of the wind speed. The flow interaction with the three major capes along the west coast of Iberia (Finisterre, Roca, and Saint Vincent) is responsible for local higher occurrence of the IPCJ, as well as the downwind intensification of the wind speed. The along-coast west Iberia wind speed structure due to IPCJ occurrences is shown. The diurnal cycle of the IPCJ is studied in the extended boreal summer (May–September), with higher mean IPCJ occurrences and jet wind speeds in late the afternoon. [ABSTRACT FROM AUTHOR]

This study proposes a dynamically downscaled climatology of Portugal, produced by a high resolution (9 km) WRF simulation, forced by 20 years of ERA-Interim reanalysis (1989-2008), nested in an intermediate domain with 27 km of resolution. The Portuguese mainland is characterized by large precipitation gradients, with observed mean annual precipitation ranging from about 400 to over 2,200 mm, with a very wet northwest and rather dry southeast, largely explained by orographic processes. Model results are compared with all available stations with continuous records, comprising daily information in 32 stations for temperature and 308 for precipitation, through the computation of mean climatologies, standard statistical errors on daily to seasonally timescales, and distributions of extreme events. Results show that WRF at 9 km outperforms ERA-Interim in all analyzed variables, with good results in the representation of the annual cycles in each region. The biases of minimum and maximum temperature are reduced, with improvement of the description of temperature variability at the extreme range of its distribution. The largest gain of the high resolution simulations is visible in the rainiest regions of Portugal, where orographic enhancement is crucial. These improvements are striking in the high ranking percentiles in all seasons, describing extreme precipitation events. WRF results at 9 km compare favorably with published results supporting its use as a high-resolution regional climate model. This higher resolution allows a better representation of extreme events that are of major importance to develop mitigation/adaptation strategies by policy makers and downstream users of regional climate models in applications such as flash floods or heat waves. [ABSTRACT FROM AUTHOR]