Accelerated loss of the sea-ice cover and increased human activities in the Arctic emphasize the need for skillful prediction of sea-ice conditions at subseasonal to seasonal (S2S) timescales. To assess the quality of predictions, dynamical forecast systems can be benchmarked against reference forecasts based on present and past observations of the ice edge. However, the simplest types of reference forecasts—persistence of the present state and climatology—do not exploit the observations optimally and thus lead to an overestimation of forecast skill. For spatial objects such as the ice-edge location, the development of damped-persistence forecasts that combine persistence and climatology in a meaningful way poses a challenge. We have developed a probabilistic reference forecast method that combines the climatologically derived probability of ice presence with initial anomalies of the ice-edge location, both derived from satellite sea-ice concentration data. No other observations, such as sea-surface temperature or sea-ice thickness, are used. We have tested and optimized the method based on minimization of the Spatial Probability Score. The resulting Spatial Damped Anomaly Persistence forecasts clearly outperform both simple persistence and climatology at subseasonal timescales. The benchmark is about as skillful as the best-performing dynamical forecast system in the S2S database. Despite using only sea-ice concentration observations, the method provides a challenging benchmark to assess the added value of dynamical forecast systems.