The gridding of daily accumulated precipitation from ground based station observations is problematic due to the fractal nature of precipitation, and therefore long period return value estimates based on such gridded daily data sets are understated. In this paper, we characterize high-resolution trends in observed extreme precipitation from 1950 to 2017 for the contiguous United States using station data only, utilizing spatial statistical methods that allow us to derive gridded estimates that do not smooth extremes and whose statistics are consistent with those of the original station data. Furthermore, we use a robust statistical technique to identify significant changes in the climatology of extreme precipitation while carefully controlling the rate of false positives. The use of spatial statistics yields both an increased signal to noise ratio for the trends as well as insight into the physical behavior of the trends themselves. While our main result involves seasonal trends, we also present annual trends in the statistics of extreme precipitation and discuss changes in the timing of the annual maxima. The results presented here only seek to detect trends, and we leave attribution of the underlying causes of these changes for future work.