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Extreme event attribution characterizes how anthropogenic climate change mayhave influenced the probability and magnitude of selected individual extremeweather and climate events. Attribution statements often involve quantificationof the fraction of attributable risk (FAR) or the risk ratio (RR) andassociated confidence intervals. Many such analyses use climate model output tocharacterize extreme event behavior with and without anthropogenic influence.However, such climate models may have biases in their representation of extremeevents. To account for discrepancies in the probabilities of extreme eventsbetween observational datasets and model datasets, we demonstrate anappropriate rescaling of the model output based on the quantiles of thedatasets to estimate an adjusted risk ratio. Our methodology accounts forvarious components of uncertainty in estimation of the risk ratio. Inparticular, we present an approach to construct a one-sided confidence intervalon the lower bound of the risk ratio when the estimated risk ratio is infinity.We demonstrate the methodology using the summer 2011 central US heatwave andoutput from the Community Earth System Model. In this example, we find that thelower bound of the risk ratio is relatively insensitive to the magnitude andprobability of the actual event.

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements