  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 daily data sets are generally
underestimated. In this paper, we characterize high-resolution changes in
observed extreme precipitation from 1950 to 2017 for the contiguous United
States (CONUS) based on in situ measurements only. Our analysis utilizes
spatial statistical methods that allow us to derive gridded estimates that do
not smooth extreme daily measurements and are consistent with statistics from
the original station data while increasing the resulting signal to noise ratio.
Furthermore, we use a robust statistical technique to identify significant
pointwise changes in the climatology of extreme precipitation while carefully
controlling the rate of false positives. We present and discuss seasonal
changes in the statistics of extreme precipitation: the largest and most
spatially-coherent pointwise changes are in fall (SON), with approximately 33%
of CONUS exhibiting significant changes (in an absolute sense). Other seasons
display very few meaningful pointwise changes (in either a relative or absolute
sense), illustrating the difficulty in detecting pointwise changes in extreme
precipitation based on in situ measurements. While our main result involves
seasonal changes, we also present and discuss annual changes in the statistics
of extreme precipitation. In this paper we only seek to detect changes over
time and leave attribution of the underlying causes of these changes for future
work.
