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The gridding of daily accumulated precipitation -- especially extremes --from ground-based station observations is problematic due to the fractal natureof precipitation, and therefore estimates of long period return values andtheir changes based on such gridded daily data sets are generallyunderestimated. In this paper, we characterize high-resolution changes inobserved extreme precipitation from 1950 to 2017 for the contiguous UnitedStates (CONUS) based on in situ measurements only. Our analysis utilizesspatial statistical methods that allow us to derive gridded estimates that donot smooth extreme daily measurements and are consistent with statistics fromthe original station data while increasing the resulting signal to noise ratio.Furthermore, we use a robust statistical technique to identify significantpointwise changes in the climatology of extreme precipitation while carefullycontrolling the rate of false positives. We present and discuss seasonalchanges in the statistics of extreme precipitation: the largest and mostspatially-coherent pointwise changes are in fall (SON), with approximately 33%of CONUS exhibiting significant changes (in an absolute sense). Other seasonsdisplay very few meaningful pointwise changes (in either a relative or absolutesense), illustrating the difficulty in detecting pointwise changes in extremeprecipitation based on in situ measurements. While our main result involvesseasonal changes, we also present and discuss annual changes in the statisticsof extreme precipitation. In this paper we only seek to detect changes overtime and leave attribution of the underlying causes of these changes for futurework.

Detected changes in precipitation extremes at their native scales derived from in situ measurements