The gridding of daily accumulated precipitation--especially extremes--from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates of long period return values and their changes based on such gridded data sets are generally underestimated. To address this issue, we present a method for deriving “probabilistic” high-resolution data sets that specifically characterize the climatological properties of extreme precipitation by applying spatial analyses to the extreme statistics of daily precipitation. Our methodology is appropriate for heterogeneous spatial domains and furthermore is scalable to very large networks of weather stations. An important application is the detection of temporal changes in the climatology of extreme precipitation, for which we develop a robust technique to identify significant pointwise changes while carefully controlling the rate of false positives. All uncertainty quantification is based on resampling methods, and we utilize supercomputing to quickly analyze, conduct inference, and detect seasonal changes in extremes for a network of approximately 5000 weather stations from the GHCN over CONUS.