In this paper, we study possible impacts of anthropogenic greenhouse gas (GHG) emissions on the 21st century climate on the continental USA using the MIT Integrated Global System Model (IGSM) framework. Climate change simulations use an emissions scenario developed with the IGSM’s Economic Projection and Policy Analysis (EPPA) Model. The scenario represents a global emission path consistent with the current view on the trajectories of technological and economic development. The estimates of possible changes in climate are based on an ensemble of 400 simulations with the IGSM’s MIT Earth System Model (MESM), a model of intermediate complexity. Regional changes over the USA were obtained using statistical downscaling that incorporates results from the simulations with the CMIP5 Atmosphere-Ocean General Circulation Models (AOGCMs). The results show that under the considered emissions scenario, surface air temperature averaged over the continental USA increases by 2.6 to 4.4K by the last decade of the 21st century (90% probability interval) relative to pre-industrial temperatures, compare to 2.3 to 3.4K for the whole globe. Corresponding changes in precipitation are -0.65 to 0.34 mm/day and 0.13 to 0.22 mm/day, respectively. There is significant variation in the geographical distribution of those changes among the ensemble simulations.

Extreme precipitation events pose a significant threat to public safety, natural and managed resources, and the functioning of society. Changes in such high-impact, low-probability events have profound implications for decision-making, preparation and costs of mitigation and adaptation efforts. Understanding how extreme precipitation events will change in the future and enabling consistent and robust projections is therefore important for the public and policymakers as we prepare for consequences of climate change.

Projection of extreme precipitation events, however, particularly at the local scale, presents a critical challenge: the climate model-based simulations of precipitation that we currently rely on for such projections—general circulation models (GCMs)—are not very realistic, mainly due to the models’ coarse spatial resolution. This coarse resolution precludes adequate representation of highly influential, small-scale features such as moisture convection and topography. Regional circulation models (RCMs) provide much higher resolution and better representation of such features, and are thus often perceived as an optimum approach to producing more accurate heavy precipitation statistics than GCMs. However, they are much more computationally intensive, time-consuming and expensive to run.

In a previous paper, the researchers developed an algorithm that detects the occurrence of heavy precipitation events based on climate models’ well-resolved, large-scale atmospheric circulation conditions associated with those events—rather than relying on these models’ simulated precipitation. The algorithm’s results corresponded with observations with much greater precision than the model-simulated precipitation.

In this paper, the researchers show that using output from RCMs rather than GCMs for the new algorithm does not improve the precision of simulated extreme precipitation frequency. The algorithm thus presents a robust and economic way to assess extreme precipitation frequency across a broad range of GCMs and multiple climate change scenarios with minimal computational requirements.