ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose of being freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset, being obtained from a high-quality and high-density set of weather observations that are then interpolated to the case studies of interest in a 100x100m resolution grid, which is the main outcome offered in this publication. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed: ----- The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). CMIP6 MODELS Resolution Responsible Centre References ACCESS-CM2 1,875º x 1,250º Australian Community Climate and Earth System Simulator (ACCESS), Australia Bi, D. et al (2020) BCC-CSM2-MR 1,125º x 1,121º Beijing Climate Center (BCC), China Meteorological Administration, China. Wu T. et al. (2019) CanESM5 2,812º x 2,790º Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. Swart, N.C. et al. (2019) CMCC-ESM2 1,000º x 1,000º Centro Mediterraneo sui Cambiamenti Climatici (CMCC). Cherchi et al, 2018 CNRM-ESM2-1 1,406º x 1,401º CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. Seferian, R. (2019) EC-EARTH3 0,703º x 0,702º EC-EARTH Consortium EC-Earth Consortium. (2019) MPI-ESM1-2-HR 0,938º x 0,935º Max-Planck Institute for Meteorology (MPI-M), Germany. Müller et al., (2018) MRI-ESM2-0 1,125º x 1,121º Meteorological Research Institute (MRI), Japan. Yukimoto, S. et al. (2019) NorESM2-MM 1,250º x 0,942º Norwegian Climate Centre (NCC), Norway. Bentsen, M. et al. (2019) UKESM1-0-LL 1,875º x 1,250º UK Met Office, Hadley Centre, United Kingdom Good, P. et al. (2019) The climate projections have been developed over each of the observational locations that were retrieved to run the statistical downscaling. The results from these projections have been spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model considering diverse adjustments and topographic corrections. The results presented here are the median of the 10 models used, obtained for each of the 4 SSPs and each of the time periods considered in ICARIA until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. Index/name Short description Source Variable Units Threshold Thermal indicators TX90 / TX10 Warm/cold days Zhang et al. (2011) TX nd 90 / 10% HD Heat day ICARIA TX nd > 30 °C EHD Extreme heat day ICARIA TX nd > 35 °C TR Tropical nights Zhang et al. (2011) TN nd > 20 °C EQ Equatorial nights AEMet 2020, ICARIA TN nd > 25 °C IN Infernal nights ICARIA TN nd > 30 °C FD Frost days Zhang et al. (2011) TN nd 3 days TXm Mean maximum temperatures ICARIA TX °C - TNm Mean minimum temperatures ICARIA TN °C - TM Mean temperatures ICARIA TA °C - HWle Heatwave length ICARIA TX nd 3d > 95% TX HWim/HWix Mean and maximum heatwave intensity ICARIA TX °C 3d > 95% TX HWf Heatwave frequency ICARIA TX ne 3d > 95% TX HWd Heatwave days ICARIA TX nd 3d > 95% TX HI - P90 Heat Index (percentile 90) NWS (1994) TX, RH °C TX>27 °C, HR> 40% UTCI Universal Thermal Climate Index Bröde et al. (2012) TARH, W - - UHI Isla de calor (BCN) anual y estacional AMB, Metrobs 2015 T °C TM1-TM2 > 0 °C Precipitation indicators R20 Number of heavy precipitation days Zhang et al. (2011) P nd >20 mm R50, R100 Days with extreme heavy rain AMB et al. (2017) P nd >50mm >100mm Ra Yearly and seasonal rainfall relative change ICARIA P mm ≥ 0.1mm IDF - CCF IDF Curves - Climate Change Factor Arnbjerg-Nielsen (2012) P - ≥ 0.1mm Forest fire indicators Mean FWI Mean Canadian FWI in fire season Stock, B.J. et al. (1989) RHn, TX, P, W . June-September Very High FWI Very High Canadian FWI Stock, B.J. et al. (1989) RHn, TX, P, W nd FWI > 38 Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. Index/name Short description Source Variable Units Threshold Drought indicators CDDx Maximum dry spell duration Zhang et al. (2011) P nd < 1 mm CDDm Mean dry spell duration Zhang et al. (2011) P nd < 1 mm SPI SPI of 1, 3, 6, 12, 24 & 36 months McKee et al. (1993) P, TA mm ≥ 0.1mm SPEI SPEI of 1, 3, 6, 12, 24 & 36 months Vicente-Serrano et al. (2010) P, TA mm ≥ 0.1mm Oceanic indicators SS Storm surge Bryant et al. (2016) MT cm - OW Significant/maximum wave height ICARIA WH m - Wind indicators EWG Extreme wind gusts ICARIA W km/h -