Precipitation and temperature datasets are essential for diverse applications, yet existing high-resolution products often lack robust uncertainty quantification and transparent, reproducible station processing. This dataset resource contains a daily multi-decadal 0.02 ensemble of gridded surface precipitation and temperature for the Contiguous United States (CONUS). It currently spans 1950-2023 (74 years) and includes 10 ensemble members and statistics based on a larger 30-member ensemble. Links to software to generate additional ensemble members are provided in the documentation. The dataset was created using a framework combining meteorological station record reconstruction and spatial probabilistic estimation. The resulting serially-complete station record archive merges high-density observing networks (GHCN-D and MADIS) using a comprehensive set of gap-filling and reconstruction techniques -- quantile mapping, interpolation, machine learning, and multi-source merging -- yielding 25,887 precipitation and 20,998 temperature stations. Gridded Ensemble fields for daily precipitation total, mean temperature (Tmean), and daily temperature range (Trange) are generated using a probabilistic geospatial estimation approach that characterizes spatial variable uncertainty via cross-validation. This open-access ensemble -- together with its station archive and gridded deterministic and probabilistic components -- provides a critical resource for high-resolution hydrologic and climate research across CONUS.