AbstractThe climate is an aggregate of the mean and variability of a range of meteorological variables, notably temperature (T) and precipitation (P). While the impacts of an increase in global mean surface temperature (GMST) are commonly quantified through changes in regional means and extreme value distributions, a concurrent shift in the shapes of the distributions of daily T and P is arguably equally important. Here, we employ a 30‐member ensemble of coupled climate model simulations (CESM1 LENS) to consistently quantify the changes of regionally and seasonally resolved probability density functions of daily T and P as function of GMST. Focusing on aggregate regions covering both populated and rural zones, we identify large regional and seasonal diversity in the probability density functions and quantify where CESM1 projects the most noticeable changes compared to the preindustrial era. As global temperature increases, Europe and the United States are projected to see a rapid reduction in wintertime cold days, and East Asia to experience a strong increase in intense summertime precipitation. Southern Africa may see a shift to a more intrinsically variable climate but with little change in mean properties. The sensitivities of Arctic and African intrinsic variability to GMST are found to be particularly high. Our results highlight the need to further quantify future changes to daily temperature and precipitation distributions as an integral part of preparing for the societal and ecological impacts of climate change and show how large ensemble simulations can be a useful tool for such research.