Variability in Projected North American Mean and Extreme Temperature and Precipitation Trends for the 21st Century: Model-To-Model Differences Versus Internal Variability

While climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model-to-model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3-7.0 emissions scenario. While model-to-model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach-a type of regression-based analysis-is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high-impact storylines. For example, model simulations with large positive GMST and El Nino-Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region. Plain Language Summary As the globe warms over the 21st century as a result of increased human greenhouse gas emissions, changes in mean and extreme temperature and precipitation are expected across North America. Projecting these changes, however, is challenging due to large uncertainty in future emissions, computer model physics, and inherent uncertainty in the climate known as internal variability. The goal of this study is to investigate future mean and extreme temperature and precipitation trends and discern the type of uncertainty that best explains the variability in future projections. Importantly, variance in future projections that is the result of model physics uncertainty can be constrained with model improvement, while variance in future projections due to internal variability cannot. For example, excluding models with unreasonably high levels of future global warming would reduce the uncertainty in future regional temperature trends and trends in the length of dry periods in the American Southwest.