Extratropical cyclones (ETCs) are strongly influenced by moist processes, rendering the impact of diabatic heating critical for forecast performance. We systematically evaluate short-range (12-hour) forecast biases of wintertime maritime ETCs over the North Atlantic, North Pacific, and Southern Ocean for the period 2023–2024. Employing a cyclone-centred composite framework, cyclones are categorised into strong and weak diabatic heating groups. We compare forecasts from the ECMWF high resolution operational 9-km Integrated Forecasting System (IFS) and the data-driven Artificial Intelligence Forecasting System (AIFS).</p> <p>Both the higher resolution of IFS and the data-driven AIFS significantly reduce the ETC propagation biases previously identified in ERA5. However, both models exhibit more pronounced errors in cyclones with strong diabatic heating. In the physics-based IFS, while the previous severe dry and cold biases are largely improved, the model still underestimates cyclone intensity and warm sector wind speeds. Furthermore, IFS displays a distinct spiral-shaped positive bias in the 850–500 hPa temperature difference, suggesting a misrepresentation of the vertical distribution and depth of diabatic heating. AIFS generally yields similar but smaller biases in most fields compared to IFS. Despite these improvements, AIFS features a notable physical inconsistency as it demonstrates a weaker mean sea level pressure (MSLP) bias but a stronger 10 m wind bias compared to IFS. This is related to an underestimation of the near-surface ageostrophic wind speed.