Small‑scale variability (3–60 km) in Arctic sea‑ice thickness plays a crucial role in sea‑ice predictability and in the climate system. However, these scales are neither directly observed nor adequately represented in climate models. While coarse‑resolution observational products (e.g., CS2SMOS) and some high‑resolution model simulations exist, bridging the scale gap remains challenging.</p> <p>In this work, we use machine learning to develop a super‑resolution algorithm that reconstructs small‑scale sea‑ice thickness features from low‑resolution input fields. The algorithm is trained on realistic high‑resolution model simulations and is based on diffusion models conditioned on low‑resolution observations. This class of models is inherently probabilistic, enabling the generation of an ensemble of plausible high‑resolution reconstructions from a single coarse‑resolution input.</p> <p>We apply the method both to model simulation, where high‑resolution ground truth is available, and to the CS2SMOS observational product. We demonstrate that the algorithm produces realistic high‑resolution sea‑ice thickness fields with improved accuracy and provides meaningful uncertainty estimates through the ensemble spread.