The regional MiKlip decadal prediction system for Europe: Hindcast skill for extremes and user‐oriented variables

Abstract

Regional climate predictions for the next decade are gaining importance, as this period falls within the planning horizon of politics, economy, and society. The potential predictability of climate indices or extremes at the regional scale is of particular interest. The German MiKlip project (“mid‐term climate forecast”) developed the first regional decadal prediction system for Europe at 0.44° resolution, based on the regional model COSMO‐CLM using global MPI‐ESM simulations as boundary conditions. We analyse the skill of this regional system focussing on extremes and user‐oriented variables. The considered quantities are related to temperature extremes, heavy precipitation, wind impacts, and the agronomy sector. Variables related to temperature (e.g., frost days, heat wave days) show high predictive skill (anomaly correlation up to 0.9) with very little dependence on lead‐time, and the skill patterns are spatially robust. The skill patterns for precipitation‐related variables (e.g., heavy precipitation days) and wind‐based indices (like storm days) are less skilful and more heterogeneous, particularly for the latter. Quantities related to the agronomy sector (e.g., growing degree days) show high predictive skill, comparable to temperature. Overall, we provide evidence that decadal predictive skill can be generally found at the regional scale also for extremes and user‐oriented variables, demonstrating how the utility of decadal predictions can be substantially enhanced. This is a very promising first step towards impact‐related modelling at the regional scale and the development of individual user‐oriented products for stakeholders.

The skill of the regional MiKlip decadal prediction system is analysed focussing on extremes and user‐oriented variables. Variables related to temperature extremes and the agronomy sector show high predictive skill with very little dependence on lead‐time. Skill patterns for precipitation‐related variables and wind‐based indices are less skilful and more heterogeneous, especially for the latter.