How Daily Temperature and Precipitation Distributions Evolve With Global Surface Temperature.

The 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. Plain Language Summary: The weather varies naturally from day to day and between regions, seasons, and years. Ecosystems and our society are both adapted to the average weather conditions of a given place and to how variable the temperature and rainfall amounts are around that average. As the global surface temperature changes, whether through natural cycles or human interference, so may this variability. In this paper, we investigate changes to the distributions of daily temperature and rainfall for different levels of surface temperature increase. By using a large set of simulations from the same climate model, we estimate their means and shapes, currently and in the near future. We find that in parts of Europe and the United States, wintertime cold days will disappear more rapidly with global warming than hot days increase, leading to a less variable climate state. In Africa and the Arctic, however, climate conditions will rapidly transition out of the range of preindustrial variability and into a climate state not yet experienced by modern society. We emphasize that in order to prepare for the impacts of climate change, we need information about changes to average properties and to extreme events and about the potential changes to daily variability itself. Key Points: Daily temperature and precipitation distributions from CESM1 Large Ensemble are shown to be functions of region, season, and surface temperatureLarge differences are found in regional distributions, where they are particularly sensitive to surface temperature anomalyWe identify regions where global warming may rapidly shift seasonal climate away from preindustrial conditions [ABSTRACT FROM AUTHOR]