The Bias Corrected CESMv1 data for current (2006-2015) and future (2091-2100) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature, relative humidity, wind speed, total precipitation, mean surface shortwave flux, top-of-atmosphere outgoing longwave radiation, mean surface latent and sensible heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 60 files (10 variables x 3 temporal resolutions x 2 periods packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 100 GB in size. The WRF version used for this project is WRF 3.8.1. . The WRF-ARW source codes and suitable tutorials are available free to users as an open-source model in the NCAR’s https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html website.

The Bias Corrected CESMv1 data for mid-century (2041-2050) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 30 files (10 variables x 3 temporal resolutions) packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

The Bias Corrected CESMv1 data for current (2006-2015) and future (2091-2100) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature, relative humidity, wind speed, total precipitation, mean surface shortwave flux, top-of-atmosphere outgoing longwave radiation, mean surface latent and sensible heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 60 files (10 variables x 3 temporal resolutions x 2 periods packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x120, and 369x369x12 data points, respectively. The entire dataset is about 100 GB in size. The WRF version used for this project is WRF 3.8.1. . The WRF-ARW source codes and suitable tutorials are available free to users as an open-source model in the NCAR’s https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html website.

The Bias Corrected CESMv1 data for mid-century (2041-2050) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 30 files (10 variables x 3 temporal resolutions) packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x120, and 369x369x12 data points, respectively. The entire dataset is about 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-NW dataset encompasses the geopolitical boundaries of the five states in the northwestern United States: Idaho, Oregon, Wyoming, Montana, and Washington. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.