Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations

Abstract

Due to inherent limitations in climate models, their output is biased in relation to observed climate and as such does not provide reliable climate projections. In this study, nine methods used to account for biases in daily precipitation are tested. First, cross-validation tests were made using a set of ENSEMBLES regional model simulations to gain insights in the potential performance of the methods in the future climate. The results show that quantile mapping type methods, being able to modify the shape of the precipitation distribution, often outperform other types of methods. Yet, as the performance depends on time of the year, location and part of the distribution considered, it is not possible to distinguish one universally best performing method. In addition, the improvement relative to the projections that would have been obtained assuming unchanged climate is relatively modest, particularly in the early twentyfirst century conditions. Further tests with different method combinations show that the projections could be potentially improved by using several well performing methods in parallel. In the second part of the study, contributions of method and model differences to the overall variation of precipitation projections are assessed. It is shown that although intermodel differences play an important role, uncertainties related to intermethod differences are substantial, particularly in the tails of the distribution. This suggests that method uncertainty should be taken into account when constructing daily precipitation projections, possibly by using several methods in parallel.