Foehn is generated over alpine terrain and is exerting increasingly pronounced impacts on air pollution, heatwaves, wildfires, and human health under global warming. Due to the complexity of influencing factors of foehn, it is difficult for traditional techniques to identify them, thereby limiting foehn's comprehensive assessments and accurate forecasting. Based on 64 years (1959–2022) of surface-station observations and reanalysis data, this study employs interpretable machine-learning techniques to systematically reveal the key controlling factors and dynamic thresholds of foehn on the eastern foothills of the Taihang Mountains, China. Results show that foehn formation is predominantly controlled by surface conditions and the influence of foehn factors varies seasonally: the leeward wind speed > 3 m s⁻¹ from 203–324° annually, the windward temperature below −17 °C in winter or 9 °C in summer, and the windward specific humidity > 0.07 g kg⁻¹ in winter or 0.75 g kg⁻¹ in summer. Synoptic analysis further reveals the weather patterns favorable for foehn occurrence, linking large-scale synoptic conditions with local thresholds for foehn formation. Foehn tends to occur under stable atmospheric stratification, with a surface high over the windward side and a low over the leeward side, together with an upper-level cold trough at 500 hPa and pronounced subsidence at 850 hPa on the leeward side on the eastern foothills of the Taihang Mountains. The findings further reveal that foehn occurrence is most favorable when the Froude number (Fr) over the Taihang Mountains falls between 0.82 and 1.5. Classical theory has only indicated that a Fr of approximately 1 favors foehn. This study, for the first time, extends this to a specific range, advancing the global understanding of foehn dynamics and offering a scientific basis for forecasting foehn and its associated compound disasters.

machine learning,eastern foothills of the Taihang Mountains,foehn,influencing factors,synoptic patterns