all edu.ucar.rda GRID NetCDF Freely Available Most datasets of surface meteorology are deterministic, yet many applications using these datasets require or can benefit from uncertainty estimates in meteorological fields. Motivated by this gap, we applied a locally-weighted spatial regression technique with the widely-used North American Land Data Assimilation version 2 (NLDAS-2) dataset values to generate ensemble estimates for daily precipitation, daily mean temperature, and diurnal temperature range. The approach is a form of ensemble dressing. This uncertainty dataset and methods from this work are made publicly available to support research such a data assimilation or model uncertainty studies. The dataset includes a 100-member ensemble for daily precipitation, temperature and diurnal temperature range at 1/8th degree for the NLDAS-2 domain (25 to 53 North, 125 to 67 West), for the time period 1979-2019. It also includes the spatial regression coefficients and other inputs needed to run the Gridded Meteorological Ensemble Tool (GMET) used to generate the ensembles. A limited number of summary statistical analyses of the dataset are also included. UCAR/NCAR/CGD UCAR/NCAR/RAL/HAP USASK/HYDROLOGY NCAR/RDA 1970-01-01T00:00:00Z