Detectability of historical trends in station‐based precipitation characteristics over the continental United States

The goal of this paper is to detect secular trends in observed, station‐based precipitation variations and extreme event occurrences over the United States. By definition, detectable trends are those that are unlikely to have arisen from internal variability alone. To represent this internal variability, we use station‐specific, seasonally varying, daily time scale stationary stochastic weather models—models in which the simulated interannual‐to‐multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period—to first estimate the trends in various means and extremes that can occur even with fixed, climatological daily precipitation characteristics. Detection of secular trends in the observed climate—whether naturally or anthropogenically induced—can then be defined relative to this stochastic variability, i.e., as trends in the means and/or extremes that are likely to have occurred only through a change in the underlying precipitation characteristics. The derived results have two important ramifications. First, they identify “hot spot” regions in which trends in precipitation characteristics are already emerging from within the envelope of stochastic variability, including (but not limited to) positive trends in annual occurrence across most of the U.S. and positive trends in annual intensity and heavy‐event accumulations across the Interior Plains and around the Great Lakes. Further, they identify “sentinel” metrics which show the greatest detectability—e.g., annual precipitation occurrence and intensity—as well as those which show the least detectability and hence are unlikely to produce any statistically meaningful signals—e.g., seasonal total precipitation and extremes.