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Changes in extreme weather may produce some of the largest societal impactsof anthropogenic climate change. However, it is intrinsically difficult toestimate changes in extreme events from the short observational record. Inthis work we use millennial runs from the Community Climate System Modelversion 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and1400 ppm CO<sub>2</sub>) conditions to examine both how extremes change in thismodel and how well these changes can be estimated as a function of runlength. We estimate changes to distributions of future temperature extremes(annual minima and annual maxima) in the contiguous United States by fittinggeneralized extreme value (GEV) distributions. Using 1000-year pre-industrialand future time series, we show that warm extremes largely change inaccordance with mean shifts in the distribution of summertime temperatures.Cold extremes warm more than mean shifts in the distribution of wintertimetemperatures, but changes in GEV location parameters are generally wellexplained by the combination of mean shifts and reduced wintertimetemperature variability. For cold extremes at inland locations, return levelsat long recurrence intervals show additional effects related to changes inthe spread and shape of GEV distributions. We then examine uncertainties thatresult from using shorter model runs. In theory, the GEV distribution canallow prediction of infrequent events using time series shorter than therecurrence interval of those events. To investigate how well this approachworks in practice, we estimate 20-, 50-, and 100-year extreme events usingsegments of varying lengths. We find that even using GEV distributions, timeseries of comparable or shorter length than the return period of interest canlead to very poor estimates. These results suggest caution when attempting touse short observational time series or model runs to infer infrequentextremes

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions