Datasets | Katherine Hegewisch

	MACA Downscaled Future Climate Projections from CMIP5 The Multivariate Adaptive Constructed Analogs (MACA) is a statistical method for downscaling Global Climate Models (GCMs) from their native coarse resolution to a higher spatial resolution. This has been used to produce two different downscaled daily gridded datasets covering CONUS from 20 CMIP5 GCMs historical scenario (1950-2005) and future scenarios (RCP4.5 and RCP 8.5) (2006-2100): MACAv2-METDATA: MACAv2 on CMIP5 utilizing the gridMET (aka METDATA) historical data as training data (4-km,1/24-deg) MACAv2-LIVNEH: MACAv2 on CMIP5 utilizing the Livneh historical data as training data (6-7-km,1/16-deg)
	TerraClimate Gridded Climate Observations TerraClimate is a gridded(1/24-deg) dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-Last Year.
	gridMET Gridded Climate Observations gridMET is a dataset of daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US from 1979-yesterday.
	CFS-gridMET Downscaled climate forecasts for next 1-28 days CFS-gridmet is a gridded dataset(~4-km, 1/24th degree) of daily forecasts for the next 1-28 days covering the contiguous US. This dataset is based on a downscaling of the Climate Forecast System (CFS)forecast model outputs utilizing the gridMET dataset as a training dataset for the downscaling.
	bcsdNMME-gridMET Downscaled climate forecasts for next 1-7 months bcsdNMME-gridmet is a gridded dataset(~4-km, 1/24th degree) of daily and monthly forecasts for the next 1-7 months covering the contiguous US. This dataset is based on a downscaling of the North American Multi-Model Ensemble (NMME) forecast model outputs utilizing the gridMET dataset as a training dataset for the downscaling and temporal daily disaggregation.