The more extreme nature of U.S. warm season climate in the recent observational record and two "well-performing" dynamically downscaled CMIP3 models

Arid and semiarid regions located in subtropical zones are projected to experience the most adverse impacts of climate change. During the warm season, observations and Intergovernmental Panel on Climate Change global climate models generally support a “wet gets wetter, dry gets drier” hypothesis in these regions, which acts to amplify the climatological transitions in the context of the annual cycle. In this study, we consider changes in U.S. early warm season precipitation in the observational record and regional climate model simulations driven by two “well‐performing” dynamically downscaled Coupled Model Intercomparison Project phase 3 (CMIP3) models (Hadley Centre Coupled Model, version 3 and Max Planck Institute (MPI) European Centre/Hamburg Model 5) that have a robust climatological representation of the North American Monsoon System (NAMS). Both observations and model results show amplification in historical seasonal transitions of temperature and precipitation associated with NAMS development, with Weather Research and Forecasting (WRF)‐MPI better representing the observed signal. Assuming the influence of remote Pacific sea surface temperature (SST) forcing associated with the El Niño–Southern Oscillation and Pacific Decadal Variability (ENSO‐PDV) on U.S. regional climate remains the same in the 21st century, similar extreme trends are also projected by WRF‐MPI for the next 30 years. A methodology is also developed to objectively analyze how climate change may be synergistically interacting with ENSO‐PDV variability during the early warm season. Our analysis suggests that interannual variability of warm season temperature and precipitation associated with Pacific SST forcing is becoming more extreme, and the signal is stronger in the observed record. Key Points Observed climate extremes are intensifying following SST natural variability WRF forced by CMIP3 GCMs has limited ability to capture long‐term observe trend RCM climate ensemble analysis must be objectively evaluated and weighted