Surface snow sublimation remains poorly quantified in the European Alps, with previous modeling studies estimating a broad range of winter surface sublimation losses (10 to 180 mm w.e.). This creates large uncertainties in the partitioning of snow ablation between sublimation and snowmelt, with significant implications for downstream water availability, as surface mass losses through sublimation directly reduce spring meltwater availability. Field observations used to quantify surface snow sublimation throughout a winter season remain scarce due to the logistical challenges posed by high-elevation Alpine environments. To address this, we estimated surface snow sublimation using the eddy-covariance method over one full winter season (November 2024 to June 2025) at the Weissfluhjoch research site (2536 m a.s.l) in the Eastern Swiss Alps by measuring water vapor fluxes with an integrated open-path gas analyzer and sonic anemometer (IRGASON). Partial correlation analyses and explainable machine learning (XGBoost model with Shapley Additive Explanations) were used to quantify the relative importance of different meteorological variables on modeled sublimation. Partial correlation analyses and explainable machine learning (XGBoost model with Shapley Additive Explanations) were used to quantify the relative importance of different meteorological variables on modeled sublimation. Over the 2024–25 winter season, cumulative net surface sublimation was 24.7 ± 18.3 mm, equivalent to 5.7 ± 4.2 % of maximum snow water equivalent, with an average daily sublimation rate of 0.15 mm d<sup>−1</sup>. Nearly half of cumulative surface sublimation occurred after peak snow height was reached, between March 31, 2025 and June 1, 2025. Vapor pressure gradient and wind speed emerged as the dominant controls on surface sublimation across both statistical and machine learning approaches, while net shortwave radiation became increasingly important following peak snow height. In contrast, air temperature and snow surface temperature showed little independent predictive power once covariance with other variables was accounted for. Our findings highlight the importance of continuous eddy-covariance measurements for quantifying snow sublimation and provide one of the first winter-season estimates of surface snow sublimation in the European Alps.