A calibration method for projecting future extremes via a linear mapping of parameters

Abstract In order to study potential impacts arising from climate change, future projections of numerical model output often must be calibrated to be comparable to observations. Rather than calibrating the data values themselves, we propose a novel statistical calibration method for extremes that assumes there exists a linear relationship between parameters associated with model output and parameters associated with observations. This approach allows us to capture uncertainty in both parameter estimates and the linear calibration, which we achieve via bootstrap. To focus on extreme behavior, we assume both model output and observations have distributions composed of a mixture model combining a Weibull distribution with a generalized Pareto distribution for the tail. A simulation study shows good coverage rates. We apply the method to project future daily-averaged river runoff at the Purgatoire River in southeastern Colorado.