Empirical evidence supports the hypothesis of a general relationship between sea temperature and recruitment of cod stocks across the North Atlantic, as well as between recruitment and the size of the spawning population. In the North Sea, cod year-class strength is inversely related to sea surface temperature during the first half of the year. This stock is currently at a low level, and the future trajectory of the stock biomass will be strongly influenced by recruitment levels. In the present study we investigate the possible use of observed and modelled sea surface temperature (SST) to increase the accuracy and/or time horizon of recruitment forecasts for this stock. We show that the statistical model developed for forecasting spring temperature has good skill (35% skill, with a standard error of 0.36°C) when predictions are made in late January. Within the frame of the current fish stock assessment working group we incorporate SST observations and January forecasts and simulate short-term recruitment projections. The resulting model accounts for a greater fraction of the variance in recruitment (42%) than that obtained without temperature (17%). In operational mode, the model allows forecasting 1.5 years in advance but the accuracy of predicted recruitment remains low. This example indicates that we have not yet reached a point where environmental information can be used with great benefit for the management of North Sea cod. However, a similar strategy may yield greater benefits if developed for other stocks for which environmental effects are better understood and/or account for a larger fraction of the variability in recruitment, for species with a shorter generation time and species for which recruitment forecast is critical to management (e.g. anchovy), and in areas where environmental prediction capabilities may be greater either in accuracy or in lead time.