This catalogue entry provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). CMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these data is mostly aimed at:

addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past.

The term "experiments" refers to the three main categories of CMIP6 simulations:

Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2014. Climate projection experiments following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically 2015-2100.

This catalogue entry provides both two- and three-dimensional data, along with an option to apply spatial and/or temporal subsetting to data requests. This is a new feature of the global climate projection dataset, which relies on compute processes run simultaneously in the ESGF nodes, where the data are originally located. The data are produced by the participating institutes of the CMIP6 project.

This dataset provides statistical indicators of tides, storm surges and sea level that can be used to characterize global sea level in present-day conditions and also to assess changes under climate change. The indicators calculated include extreme-value indicators (e.g. return periods including confidence bounds for total water levels and surge levels), probability indicators (e.g. percentile for total water levels and surge levels). They provide a basis for studies aiming to evaluate sea level variability, coastal flooding, coastal erosion, and accessibility of ports at a global scale. The extreme value statistics for different return periods can be used to assess the frequency of an event and form the basis of risk assessments. The global coverage allows for world-wide assessments that are particularly useful for the data scarce regions where detailed modelling studies are currently lacking. The indicators are computed from time series data available in a related dataset in the Climate Data Store named Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections (see Related data), where further details of the modelling are provided. The indicators are produced for three different 30-year periods corresponding to historical, present, and future climate conditions (1951-1980, 1985-2014, and 2021-2050). The future period is based on global climate projections using the high-emission scenario SSP5-8.5. The dataset is based on climate forcing from ERA5 global reanalysis and 4 Global Climate Models (GCMs) of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. An estimate of the uncertainties associated with the climate forcing has been obtained through the use of a multi-model ensemble. Each of the indicators provides ensemble statistics computed across the 4 members of the HighResMIP ensemble (e.g. median, mean, standard deviation, range). Absolute and relative changes for the future period (2015-2050) relative to the present-day (1985-2014) are provided to assess climate change impacts on water levels. This dataset was produced on behalf of the Copernicus Climate Change Service.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5). The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and the CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. A total of 31 climate indices have been calculated using the CanDCS-U6 and CanDCS-M6 datasets. The climate indices include 27 Climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) and 4 additional indices that are slightly modified from the Climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the indices. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.

The new climate dataset BASD-CMIP6-PE for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 projections of 10 GCMs addresses the need for reliable high-resolution (1d, 10km) climate data covering Peru and Ecuador. This dataset includes both historical simulations (1850-2014) and future projections (2015-2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method (Lange, 2019, 2021) and data from regional observational datasets such as RAIN4PE (Fernandez-Palomino et al., 2021a, b) for precipitation and PISCO-temperature (Huerta et al., 2018) for temperatures as reference data. The Reliability of the BASD-CMIP6-PE was evaluated through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. The evaluation showed that the BASD-CMIP6-PE is reliable for describing the spatial patterns of atmospheric variables and streamflow simulation, including low and high flows. This suggests the usefulness of the new dataset for climate change impact assessment studies in Peru and Ecuador. The BASD-CMIP6-PE data are available for the domain covering Peru and Ecuador, located between 19°S and 2°N and 82-67°W, at 0.1° spatial and daily temporal resolution. The precipitation unit is mm, and the temperature is in °C. The data are in the NetCDF format and arranged by model, model member, experiment, variable, temporal resolution, and subset period (e.g., canesm5_r1i1p1f1_ssp126_pr_daily_2015_2020.nc).

This dataset contains archived 3-hourly outputs from wave climate simulations at 0.5-degree global grid resolution carried out using an Irregular-Regular-Irregular (IRI) three-grid system to model the polar regions, and implemented in WaveWatch III (v6.07). The model configuration is outlined in manuscripts: Meucci A., Young I. R., Hemer M., Trenham C., and Watterson I. 2023. 140 Years of Global Ocean Wind-wave Climate Derived from CMIP6 ACCESS-CM2 and EC-Earth3 GCMs. Global Trends, Regional Changes, and Future Projections. Journal of Climate, 1-56. DOI: https://doi.org/10.1175/JCLI-D-21-0929.1

Wave model forcing consisted of surface wind and sea-ice concentration fields from two CMIP6 models. The model is run with the latest observation-based source term parametrization ST6, using two different values of the wind-drag coefficient "CDFAC" parameter: 1 and 1.08.

Archived variables, in monthly netCDF files for all simulations, include wind speed, significant wave height, second order mean wave period, peak frequency, mean and peak wave direction, wave energy, and 3 partitions of significant wave height.

In total, this dataset contains over 900 simulated model years of data.

Gridded field data are provided in this collection. Directional spectra output points are available on request.

Directory Structure: UniMelb-CSIRO_CMIP6_projections CMIP6 historical (spans 1961-2014) -Historical - ACCESS-CM2 (CDFAC1, CDFAC1.08) - EC-EARTH3 (CDFAC1, CDFAC1.08) CMIP6 projections (spans 2015-2100) -SSP1-26 - ACCESS-CM2 (CDFAC1, CDFAC1.08) - EC-EARTH3 (CDFAC1, CDFAC1.08) -SSP5-85 - ACCESS-CM2 (CDFAC1, CDFAC1.08) - EC-EARTH3 (CDFAC1, CDFAC1.08) Lineage: The wave model is forced with 3-hourly surface winds and linearly interpolated daily sea-ice concentration fields taken from the designated forcing CMIP6 General Circulation Models.

Output is produced on the IRI grid as well as at point locations every 10 degrees globally and in higher resolution rings around Australia as a whole, and the SE corner (Vic-Tas).

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and the CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. A total of 31 climate indices have been calculated using the CanDCS-U6 and CanDCS-M6 datasets. The climate indices include 27 Climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) and 4 additional indices that are slightly modified from the Climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the index. Monthly indices are also available in seasonal and annual versions. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The r ...

Supplementary information files for "A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses"A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.©The Author(s) CC BY 4.0

This collection consists of application-ready climate projections based on observed climate variability, adjusted through a quantile-delta-change method to incorporate projections from CMIP6 climate models.

The collection consists of daily projections of precipitation, mean downwelling shortwave radiation, maximum and minimum near-surface temperature, maximum, mean and minimum near-surface relative humidity, and mean and maximum near-surface wind speed for 9 CMIP6 models. Up to four 20-year windows of projections are available per model, corresponding to global warming levels of 1.5°C, 2°C, 3°C, and 4°C above pre-industrial (1850-1900). Data are based on future climate changes derived from simulations of the SSP3-7.0 shared socio-economic pathway and BARRA-R2 historic climate variability for Australia. This dataset is on the original 11km BARRA-R2 resolution.

Historical baseline data from BARRA-R2 used for the quantile-delta-change adjustment are also available.

More in-depth information on the dataset can be found in the technical report: https://doi.org/10.25919/03by-9y62

This collection is not updated frequently. Lineage: Daily data from 9 CMIP6 models (ACCESS-CM2, ACCESS-ESM1-5, CESM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3, MPI-ESM1-2-HR, NorESM2-MM, UKESM1-0-LL) are used to calculate climate changes between a 2001-2020 baseline period (corresponding to the 1°C global warming level) and the future projection periods for each SSP corresponding to the 1.5°C, 2°C, 3°C, and 4°C global warming levels. The projected changes are then applied to 2001-2020 baseline data from BARRA-R2 to produce the application-ready datasets. The quantile-delta-change method is used, which applies different climate changes to different parts (quantiles) of the distribution of daily data.

This process is done using python software available at https://github.com/AusClimateService/qq-workflows/tree/main/qdc-cmip6.

More in-depth information on the method can be found in the technical report: https://doi.org/10.25919/03by-9y62

The application-ready projections have also been subject to a quality-control and assurance check utilising a python script (https://github.com/climate-innovation-hub/qdc-cmip6-qaqc) to ensure full data coverage and compliance with metadata standards, with the process documented in the QAQC report: https://doi.org/10.25919%2F4n26-fh08

A monthly water-balance model (MWBM) is applied to simulate components of the water balance for the period 1950-2100 under ssp245, ssp370, and ssp585 scenarios for the Contiguous United States. The statistically downscaled LOCA2 temperature and precipitation projections from 27 GCMs from the Climate Model Intercomparison Program Phase 6 (CMIP6) are use as input to the water balance model. This data set supports the USGS National Climate Change Viewer (ver. 2). The statistically downscaled data set is: CMIP6-LOCA2: Localized Constructed Analogs (Pierce et al. 2023, bias corrected by a modified version of Livneh et al. 2013) Users interested in the downscaled temperature and precipitation files are referred to the data set home page: LOCA: https://loca.ucsd.edu Bias correction data set: https://cirrus.ucsd.edu/~pierce/nonsplit_precip/ The 27 included GCMs are: ACCESS-CM2, ACCESS-ESM1-5, AWI-CM-1-1-MR, BCC-CSM2-MR, CESM2-LENS, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, CanESM5, EC-Eart ...

Data and code supporting the research article:High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method -
Fasil M. Rettie, Sebastian Gayler, Tobias KD Weber, Kindie Tesfaye, Thilo Streck. Please, find detail description of the codes and datasets in readme file.

Environment and Climate Change Canada’s (ECCC) CMIP6 statistically downscaled agroclimatic indices are an updated version of the CMIP5 agroclimatic indices dataset making use of the new set of downscaled scenarios (Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6)) created by the Pacific Climate Impacts Consortium (PCIC). To address the needs of different user groups in Canada, 49 indices, including agroclimatic indices, were proposed by the Canadian adaptation community through a series of consultations. Please see the definition list for the equations of each index. The range of impact-relevant climate indices available for download includes, indices representing counts of the number of days when temperature or precipitation exceeds (or is below) a threshold value; the episode length when a particular weather/climate condition occurs; and indices that accumulate temperature departures above or below a fixed threshold. The statistically downscaled climate indices are available for individual models and ensembles, historical simulations (1951-2014) and three new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs), SSP1-2.6, SSP2-4.5, and SSP5-8.5 (2015-2100), at a 10 x 10 km degree grid resolution. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impact assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with a finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

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:

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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.

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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).

Geospatial climate change projections are critical for assessing climate change impacts and adaptations across a wide range of disciplines. Here we present monthly-based grids of climate change projections at a 2-km resolution covering Canada and the United States. These data products are based on outputs from the 6th Coupled Model Intercomparison Project (CMIP6) and include projections for 13 General Circulation Models (GCMs) , three Shared Socio-economic Pathways (SSP1 2.6, SSP2 4.5, and SSP5 8.5), four 30-year time periods (2011-2040, 2021-2050, 2041-2070, and 2071-2100), and a suite of climate variables, including monthly maximum and minimum temperature, precipitation, climate moisture index, and various bioclimatic summaries. The products employ a delta downscaling method, which combines historical normal values at climate stations with broad-scale change projections (or deltas) from GCMs, followed by spatial interpolation using ANUSPLIN. Various quality control efforts, described herein, were undertaken to ensure that the final products provided reasonable estimates of future climate.

CRISI-ADAPT II 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. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. For further details, find here a brief of the methodology followed: Methodology Information provided by 10 models belonging to CMIP6 have been included. 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. Information about the ten climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the sixth report of the IPCC. Models were supplied by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives. CMPI6 MODELS Resolution Responsible Centre References 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) 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) GFDL-ESM4 1,250º x 1,000º National Oceanic and Atmospheric Administration (NOAA), E.E.U.U. Krasting, J.P. et al. (2018) MPI-ESM1-2-HR 0,938º x 0,935º Max-Planck Institute for Meteorology (MPI-M), Germany. Von Storch, J. et al. (2017) MRI-ESM2-0 1,125º x 1,121º Meteorological Research Institute (MRI), Japan. Yukimoto, S. et al. (2019) UKESM1-0-LL 1,875º x 1,250º Uk Met Office, Hadley Centre, United Kingdom Good, P. et al. (2019) NorESM2-MM 1,250º x 0,942º Norwegian Climate Centre (NCC), Norway. Bentsen, M. et al. (2019) ACCESS-ESM1-5 1,875º x 1,250º Australian Community Climate and Earth System Simulator (ACCESS), Australia Ziehn, T. et al. (2019) Since the case studies are distributed among Portugal, Spain, Italy, Malta and Cyprus, a grid covering the whole Mediterranean area, between latitudes 30°N and 50°N and longitudes between 15°W and 40°E, has been chosen for the study. The atmospheric variables available from CMIP6 are wind, temperature, humidity and rainfall at a daily timescale and sea level rise at a monthly timescale. However, it is possible simulate sub-daily rainfall (e.g. for the sector of Flooding and Emergency Response) thanks to the index-n method (Monjo et al. 2016). Other variables such as fog and wave height requires to be obtained from model post-processing. In addition to these models, information has also been combined to the ERA5-LAND, which has a resolution of 0.07°×0.07°. For each climate variable simulated by the CMIP6 models, a statistical downscaling was applied according to seven steps: Firstly, as a reference field, a purely geo-statistical downscaling of the original Era5-Land grid (0.07°×0.07°) was performed for each variable to a 1km×1km grid, using linear stepwise regression with topological and geographical parameters (altitude, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea), and bilinear model for the residual errors. For all models and their corresponding scenarios, the average values for the study area have been calculated for the periods 1981-2010, 2021-2050 and 2071-2100 and their rate of variation between the periods 2071-2100 and 2021-2050. The model scenario with the highest rate of variation and the model scenario with the lowest rate of variation have been chosen to range future variations of the variables. Quantiles 90th, 50th and 10th scenarios have been called Upper, Medium and Lower, respectively. For these scenarios, Upper, Medium and Lower, the empirical values corresponding to the return periods of 5, 10, 20 and 30 years for the periods 1981-2010, 2021-2050, 2046-2075 and 2071-2100 have been calculated for each grid point in the model. Once the above results were obtained, an interpolation to a grid of 1km×1km was performed using the bilinear method. Then, the increment or difference with respect to the same return periods of the period 1981-2010 has been calculated for each period of 30 years (2021-2050, 2046-2075 and 2071-2100) and for each return period. Relative increment (instead of absolute increment) was considered for some variable such as precipitation and wind. Finally, the absolute o relative increment of each scenario and return period (step 6) was added to the reference values of each variable (step 1), obtaining climate scenarios in a 1km×1km grid (see for instance Figure 8). This entire process, applied to return-period values, is an empirical quantile mapping by increment from reanalysis (Monjo et al. 2013).

Climate change impact assessments require local-scale climate scenarios. The climate change projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of realisations to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across the UK based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator LARS-WG 7.0. The data set provides 1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of realisations of daily weather for the baseline as well as future climate change scenarios are helpful for estimating seasonality and inter-annual variation, and for detecting short, low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.

Number of GCMs available for each scenario RCP2.6 in CMIP5 corresponds to SSP126 in CMIP6, RCP4.5 in CMIP5 corresponds to SSP245 in CMIP6 and RCP8.5 in CMIP5 corresponds to SSP585 in CMIP6.

This dataset provides the results of a global variance decomposition of downscaled and bias-corrected CMIP6 climate projections. The total projection variance for a set of climate metrics is partitioned into contributions from: scenario uncertainty, model/GCM uncertainty, downscaling and bias-correction uncertainty, and interannual variability. The contribution from each source is expressed as a percentage of the total variance. Seven climate metrics are analyzed: Annual average temperature (avg_tas.nc) Annual total precipitation (tot_pr.nc) Annual maximum of daily maximum temperature (max_tasmax.nc) Annual maximum 1-day precipitation (max_pr.nc) Annual number of extremely hot days (hot_days.nc) Annual number of extremely wet days (wet_days.nc) Annual number of dry days (dry_days.nc) Extremely hot/wet days are defined to occur when temperature/precipitation exceeds the local 99th percentile defined over 1980-2014. Dry days are defined to occur when daily precipitation is less than 1mm. all_metrics_timesliced.nc gives the results for all metrics averaged over three 20-year periods: 2020-2039, 2050-2069, 2080-2099. For more details on the methods, see: Lafferty & Sriver, Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6, npj Climate & Atmospheric Science (2023) An interactive visualization of this data can be found at: https://lafferty-sriver-2023-downscaling-uncertainty.msdlive.org

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets assist the science community in conducting studies on climate change impacts at regional scales, and enhance public understanding of possible future climate patterns of homogenous regions at spatial scales.

The data sets contain high resolution (~100 kilometres) dynamical downscaling of World Climate Research Programme (https://www.wcrp-climate.org/) CMIP6 climate projections model data for SSP (126, 245, and 585) scenarios for the period 2015-2100.

The CMIP6 precipitation and temperature data have been obtained from the Earth System Grid Federation (https://esgf-data.dkrz.de/projects/esgf-dkrz/) site. Detailed information about the data, including the terms of use, can be obtained from the CMIP6 (https://pcmdi.llnl.gov/CMIP6/) site.