Exploring the limits of multi-annual predictability for compound hot-dry extremes

Hot-dry compound extremes have recently gained attention as a result of their potential destructive impacts on environments and societies. To this end, multi-annual predictions of these events could potentially offer useful information for a variety of socio-economic sectors. However, while previous studies have successfully predicted these extremes in some regions, they still struggle to capture much of the interannual variability, with most skill stemming from long-term forcings. Here, we investigate the sources of such limitations by comparing the skill of multi-annual forecasts against a perfect-model setup, using the EC-Earth3 model. While real-world predictions are initialized towards the observed state and evaluated in their ability to predict observed climate, the perfect-model predictions are initialised and assessed against a historical simulation with the same model, ensuring physical consistency between the prediction and the reference, and avoiding the uncertainties tied to the initial conditions. By comparing the perfect-model setup (PerfSet) with the real-world setup (RealFor), we assess to what extent the inconsistencies between real-world climate and the model affect the multi-annual predictability of compound hot-dry extremes.

From a skill perspective, the relative performance of PerfSet and RealFor depends on the region analysed, with neither experiment consistently outperforming the other. Residual correlation analysis, representing the contribution of initialization to forecast skill, indicates that PerfSet generally exhibits larger areas with statistically significant correlations. These regions broadly coincide with areas where PerfSet shows higher skill, suggesting a stronger influence of initialization in this experiment. Further analyses distinguish dry conditions as a key limit to predictability for both experiments, particularly where aridity is mainly dependent on precipitation variability rather than potential evapotranspiration. These results illustrate the inherent limitations of models for multi-annual predictions and highlight how the intrinsically low predictability of precipitation constrains the predictability limits for hot-dry compound extremes, whether predicting real-world observations or a controlled reference dataset.