JGR-Atmospheres This study applies statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour \deleted[date/time, etc.]precipitation, \addedall totals, and near-surface wind speed from the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. Reliability and sharpness, and \replacedhencetherefore skill, of the former is expected to improve over time. Thus, the gain by post-processing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics \addedabbreviated as (EMOS) for each station and variable. Given the higher average skill of the post-processed forecasts, we analyse the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that post-processing will keep adding skill in the foreseeable future.