Detection, e.g. the process of demonstrating that climate has changed in some defined statistical sense first requires some means of estimating the range of internal variability absent any external drivers. Ideally, the internal variability would be derived from the observations themselves, however the observed variability is a confluence of both internal variability and variability in response to external drivers. Further, numerical climate models the standard tool for detection studies have their own estimates of intrinsic variability, which may differ substantially from that found in the observed system as well as other model systems. These problems are further compounded for weather and climate extremes, which as singular events are particularly ill-suited for detection studies because of their infrequent occurrence, limited spatial range, and underestimation within global and even regional numerical models. Here we will show how stochastic daily-precipitation 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 can be used to address many of these issues simultaneously. Through the novel application of these well-established models, we will evaluate the significance of observed trends in seasonal-mean precipitation variations and extreme event occurrences over the United States. In turn, our results will highlight the location and timing of sentinel regions and seasons in which detectable trends in precipitation characteristics are already emerging from the envelope of interannual to decadal variability.