This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D04 (Catamayo-Chira Basin, Ecuador and Peru). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D03 (Tekeze-Atbara and Blue Nile Basins, Sudan and Ethiopia). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D02 (Rio São Francisco, Brazil). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D01 (Karun Basin, Iran). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D02 (Rio São Francisco, Brazil). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D01 (Karun Basin, Iran). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D03 (Tekeze-Atbara and Blue Nile Basins, Sudan and Ethiopia). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D04 (Catamayo-Chira Basin, Ecuador and Peru). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.

An ensemble of bias adjusted regional climate model simulations based on EURO-CORDEX (CORDEX-EUR11). The data set covers daily temperature (minimum, average and maximum) and precipitation for historical, rcp26, rcp45 and rcp85 experiments covering a period from 1971 to 2100. In total 8 different RCMs from 8 institutes are included in the data set. ISIMIP3BASD v2.4.1 (https://doi.org/10.5281/zenodo.4686991) method was used for bias adjustment. The method is based on a parametric quantile mapping, including trend preservation of each quantile. Bias adjustment was performed for each variable separately. We used E-OBS v19.0e (https://doi.org/10.1029/2017JD028200) data to calibrate the bias adjustment transfer functions for the period 1971 to 2005. We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups (CLMcom, DMI, GERICS, IPSL-INERIS, KNMI, MPI-CSC, SMHI and UHOH) for producing and making available their model output. 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 data was developed and utilized within the Clim4Vitis (https://clim4vitis.eu), ProgRAMM and OptAKlim projects. The project is supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support programme. ProgRAMM: 281B204516 OptAKlim: 281B203216

We develop a self-consistent, large ensemble, high-resolution, bias-corrected global dataset of future climates for a set of four possible 21st century scenarios, which is suitable for assessing local-scale climate change impacts and climate policy benefits from a risk-based perspective across different applications. Four emission scenarios represent the existing energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. We employ the MIT Integrated Global System Modeling (IGSM) framework, which consists of the MIT Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model (EPPA). The EPPA characterizes detailed economic activities to track inter-sectoral and inter-regional links, while the MESM represents key physical, chemical, and biological components of the Earth system that are impacted by human activity. Such integrated framework ensures consistent treatment of interactions among population growth, economic development, energy and land system changes and physical climate responses, which can provide improved assessments of climate impacts and climate policy benefits across multiple sectors. The MESM contains a two-dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly-diffusing ocean model. This architecture allows for conducting a large ensemble of climate simulations for robust uncertainty analyses at significantly less computational cost than state-of-the-art climate models. In addition, we apply a combined spatial disaggregation (SD) – bias correction (BC) delta method with SD for achieving the high resolution and BC for correcting the biases inherent in the MESM future climate projections. The delta method adds the anomalies or deltas (future climate trends) onto a historical, detrended climate that is based on the third phase of the Global Soil Wetness Project (GSWP3, http://hydro.iis.u-tokyo.ac.jp/GSWP3/). The anomalies or deltas are derived by spatially disaggregating the IGSM zonal climate projections based on regional hydroclimate change patterns from the 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. For each emission scenario, a distribution of plausible trajectories is provided by a 50-member ensemble to represent the uncertainty in the Earth system (e.g., the climate sensitivity, rate of heat uptake by the ocean, uncertainty in carbon cycle), allowing for constructing a 900-member ensemble of regional climate outcomes. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. A technical evaluation indicates the dataset well represents the expected large-scale climate features across various regions of the globe and can meet various needs associated with climate impact assessments, including uncertainty analyses, risk quantification, climate policy mitigation, and driving climate impact models which require monthly data inputs, on both global and regional scales. There is no model version. But all the developed models are available online (https://globalchange.mit.edu/research/research-tools/earth-system-model) and have relevant licenses. On the website you could find the following information: The source code of the MESM is publicly available for non-commercial research and educational purposes via github (i.e. github.com:mit-jp/igsm.git). Under this open source protocol, we have also established a software license through the MIT Technology Licensing Office. As the MESM has embedded models developed at three other institutions, appropriate copyright clearances for the third-party code are required.