Exploring the performance of bias correction applied outside the calibration period’s climate regime

It is common practice to apply some form of bias correction to climate models before use in impact modelling, such as hydrology. The standard method is to evaluate the correction method based on a cross validation procedure with two or more sub-periods. This allows the method to be assessed on data not previously seen in the calibration step. However, with standard split-sample setups, the data is most likely in a similar climate regime as the calibration data. In effect, the method is evaluated in the same climate regime as it is calibrated, and informs little about the performance outside the current climate regime.

To address this issue, a discrete split sample test (DSST) was set up so that as diverse climate regimes as possible were sampled. The simplest climate analogue would be to perform the DSST on the coldest years and evaluate on the warmest, to mimic a changing temperature. Here, the tests are extended to more exotic indicators, such as snow pack, the joint probability of wet and cold seasons, the number of hot days in a year, the convective activity during summer; all related to a specific case study issue. Six different bias correction methods of both standard quantile mapping and other approaches to scale the reference time series are included. The methods are applied in a pseudo-reality framework to six climate model projections from Euro-CORDEX 12.5 km simulations. The analysis is focused on comparing the DSST performance with the impact on the climate change signals, and to the reliability of each method when applied to different climate regimes.