Global warming is expected to alter the frequency of extreme precipitation events. Such changes could have substantial ecological, economic, and sociological consequences. However, climate models in general do not correctly reproduce the frequency distribution of precipitation, especially at the regional scale. In this study, gridded data from a dense network of surface precipitation gauges and a global atmospheric reanalysis at a coarser scale are combined to develop a diagnostic framework for the large-scale meteorological conditions (i.e. flow features, moisture supply) that dominate during extreme precipitation. Such diagnostic framework (based on the climate extreme analogue) is evaluated against the relevant daily reanalysis meteorological fields and achieve a success rate of around 80% in detecting observed extreme events within one or two days. When applied to the late 20th century climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the analogue method is found to produce more consistent and less uncertain number of seasonal extreme events with the observations as opposed to using model-simulated precipitation over the south-central United States, the Midwestern, and the Western United States examined in this study. The analogue method is further applied to the CMIP5 multi-model projections for two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5) to assess the potential future changes in the probability of precipitation extremes over the same study regions. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess extreme precipitation frequency.