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There are many climate change scenarios that are of interest to explore by climate models, but computational power limits the total number of model runs. Pattern scaling is a useful approach to approximate mean changes in climate model projections, and we extend this methodology to build a climate model emulator that also accounts for variability of temperature projections at the seasonal scale. Using 30 runs from the NCAR/DOE CESM1 large initial condition ensemble for RCP8.5 from 2006 to 2080, we fit a pattern scaling model to grid-specific seasonal average temperature change. We then use this fitted model to emulate seasonal average temperature change for the RCP4.5 scenario based on its global average temperature trend. By using a linear mixed-effects model and carefully resampling the residuals from the RCP8.5 model, we emulate the variability of RCP4.5 and allow the variability to depend on global average temperature. Specifically, we emulate both the internal variability affecting the long-term trends across initial condition ensemble members, and the variability superimposed on the long-term trend within individual ensemble members. The 15 initial condition ensemble members available for RCP4.5 from the same climate model are then used to validate the emulator. We view this approach as a step forward in providing relevant climate information for avoided impacts studies, and more broadly for impact models, since we allow both forced changes and internal variability to play a role in determining future impact risks. [ABSTRACT FROM AUTHOR]

The characteristics associated with an exponential distribution is used as a yard stick to assess distributions for 24-hr precipitation records from 13 771 rain gauges from mainly the USA and Europe. It is shown that the daily accumulated precipitation amount is approximately similar to an exponential distribution, but not identical, and that quantiles of the distributions can to a zeroth order be specified from the mean of the wet-day precipitation. However, the data have a thicker upper tail than the exponential distribution. We propose a simple method for making a crude estimate for quantiles of wet-day 24-hr precipitation distribution, and a refinement of the exponential distribution based on principal component analysis. We also show that the high quantiles are related to the wet-day mean. The associations between the wet-day mean and quantiles from the observations are compared with results from regional climate model simulations, taken from a number of regional climate models. Similar tendencies are seen in the models as in the rain gauge data. [ABSTRACT FROM AUTHOR]