Sea ice forecasts are becoming a demanding need since human activities in the Arctic are constantly increasing and this trend is expected to continue. In this context, the recent availability of the Subseasonal to Seasonal Prediction Project (S2S) Dataset has a particularly good timing and provides a solid base to make an initial assessment of the predictive skills of probabilistic forecast systems with dynamical sea ice. In this study, we employ different verification metrics to compare the S2S sea ice forecasts with satellite observations and the models’ own analyses. In particular, the focus is on the sea ice spatial distribution in the Arctic, which is relevant information for potential final users. The verification metrics, specifically chosen to quantify the quality of the forecasted sea ice edge position, are the Integrated Ice Edge Error (IIEE), the Spatial Probability Score (SPS) and the Modified Hausdorff Distance (MHD). Despite the early development stage of Arctic sea ice predictions on the seasonal time scale, and the fact that the main focus of the S2S systems is mostly not on sea ice per se, our findings reveal that some of the S2S models are promising, exhibiting better predictive skills than the observation-based climatology and persistence. However, the results also point to critical aspects concerning the data assimilation procedure and the tuning of the models, which can strongly affect the forecasts quality. The comparison of different versions of the ECMWF forecast system shows the benefits brought by a coupled dynamical description of the sea ice instead of its prescription based on persistence and climatological records. Moreover, the systematic application of the verification metrics to such a broad pool of forecasts provides useful indications about strengths and limitation of the verification metrics themselves. Given the increasing availability of new and better sea ice observations and the possible improvements to coupled seasonal forecast systems, the formulation of reliable Arctic sea ice predictions for the subseasonal to seasonal time scales appears to be a realistic target for the scientific community.