Representation of Long‐Term Observed UK Winter Precipitation Trends and Variability in UKCP18 Global Climate Models

ABSTRACT

Evaluating the representation of observed precipitation trends in historical climate model simulations is challenging due to the combined effects of dynamical and thermodynamic changes and the strong influence of natural variability. Here, we assess the representation of long‐term (1901–2023) trends and variability in UK winter mean precipitation, focusing on the separation of dynamical and non‐dynamical components. We apply a dynamical adjustment methodology to the global climate models (GCMs) of the UK Climate Projections 2018 (UKCP18) to isolate circulation‐driven and non‐dynamical precipitation changes and variability. While several UKCP18 GCMs simulate a winter wetting trend broadly consistent with observations, the underlying drivers differ significantly. Modelled increases are primarily circulation‐driven, whereas observed trends are dominated by thermodynamic forcing. Also, the scaling of precipitation with temperature in ensemble members is substantially weaker than observed. While a thermodynamic signal emerges in the ensemble mean, its magnitude is less than a third of the observed rate. Consequently, these ensemble members may underestimate future non‐dynamical precipitation changes. Furthermore, the ensemble members under‐represent observed dynamical interannual variability, resulting in fewer high‐impact winters associated with persistent large‐scale atmospheric circulation anomalies. These results highlight important limitations in the representation of winter precipitation variability and change in the UKCP18 GCMs and have implications for the interpretation of model output in climate risk assessments and for the use of higher‐resolution ensembles within the UKCP18 framework.