Evaluating Machine‐Learning Weather Prediction for UK Windstorm Weather Warnings

ABSTRACT Machine‐learning weather prediction (MLWP) has emerged as a powerful new forecasting tool, with the potential to complement or even replace numerical weather prediction (NWP) in early warning systems. MLWP models are computationally cheap once trained, offering more timely, accessible, and energy‐efficient forecasts than NWP. Yet their ability to capture weather extremes, particularly strong winds associated with extra‐tropical cyclones, remains insufficiently understood. This study provides the first comprehensive evaluation of MLWP performance for dynamically diverse UK windstorms, addressing a critical evidence gap before these models can be responsibly deployed in weather risk management. We evaluate eight state‐of‐the‐art MLWP models against reanalysis, NWP analysis, and an operational NWP ensemble for six contrasting UK cyclones from winter 2023/24. We find substantial inter‐storm and inter‐model variability in forecast skill at 24‐h lead time. Forecast accuracy is found to be linked to the underlying atmospheric dynamics and spatial scales of the wind‐producing features: wind maxima associated with mesoscale structures are systematically underestimated across all MLWP models. Significant variability is also found in the representation of mesoscale storm structure, and the highest‐resolution MLWP model (Aurora‐0.1) does not deliver uniform improvements across the cyclone set. In‐depth analysis of two mature Shapiro–Keyser cyclones reveals a consistent tendency for MLWP models to smooth sharp frontal gradients and misrepresent key mesoscale features, including bent‐back warm fronts and frontal‐fracture regions. These deficiencies are physically consistent with the underestimation of localised extreme winds. Overall, our findings highlight the current limitations of MLWP for underpinning UK wind warnings. While these models are not yet suitable to replace NWP for hazardous windstorm prediction entirely, their demonstrated skill in capturing storm tracks and synoptic‐scale evolution points to promising complementary roles in future national weather forecasting and resilience frameworks.