Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two‐scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time‐varying proportionality parameters. This two‐step approach ensures that the long‐term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two‐scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags ( Gulosus aristotelis ) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200‐fold increase over 30 years for some subpopulations. The two‐scale model predicted very sensible growths, never larger than a threefold increase over the 30‐year time horizon, which is more in accordance with this species' life history. This two‐scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full‐dispersal models. Because the only data required are simple counts, this model could be useful to many large‐scale wildlife monitoring programs.