Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting | Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting

Severe precipitation events occur rarely and are often localised in space and of short duration, but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of the occurrence of these rare events. These are often projected using data from regional climate model (RCM) simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterisations in the RCMs, the simulated present-day climate usually has biases relative to observations; these biases can be in the mean and/or in the higher moments. Therefore, the RCM results are adjusted to account for these deficiencies. However, this does not guarantee that the adjusted projected results will match the future reality better, since the bias may not be stationary in a changing climate. In the present work, we evaluate different adjustment techniques in a changing climate. This is done in an inter-model cross-validation set-up in which each model simulation, in turn, performs pseudo-observations against which the remaining model simulations are adjusted and validated. The study uses hourly data from historical and RCP8.5 scenario runs from 19 model simulations from the EURO-CORDEX ensemble at a 0.11∘ resolution. Fields of return levels for selected return periods are calculated for hourly and daily timescales based on 25-year-long time slices representing the present-day (1981–2005) and end-21st-century (2075–2099). The adjustment techniques applied to the return levels are based on extreme value analysis and include climate factor and quantile-mapping approaches. Generally, we find that future return levels can be improved by adjustment, compared to obtaining them from raw scenario model data. The performance of the different methods depends on the timescale considered. On hourly timescales, the climate factor approach performs better than the quantile-mapping approaches. On daily timescales, the superior approach is to simply deduce future return levels from pseudo-observations, and the second-best choice is using the quantile-mapping approaches. These results are found in all European subregions considered. Applying the inter-model cross-validation against model ensemble medians instead of individual models does not change the overall conclusions much.

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