The demand for skillful climate predictions on subseasonal-to-multidecadal time scales is rising almost by the day, not least because the growing renewable energy sector, but also many other important socio–economic sectors are vulnerable to climate variations. Large scale atmospheric patterns in the North–Atlantic European sector, so-called teleconnections, are well known to have major influence on European climate conditions. For that reason there exists a wide variety of hybrid dynamical–statistical applications, which combine dynamical model output with teleconnections in one way or another to improve the rather modest predictive skill of state-of-the-art dynamical climate forecasts over Europe. The potential improvement generated by these kinds of postprocessing methods is naturally limited by the strength of association between the circulation patterns and the local climate parameters. We propose a statistical technique to retrieve atmospheric patterns—targeted teleconnections—that are maximally predictive for a given climate parameter in a region of choice so as to optimize the potential of statistical postprocessing. The possibility of improvement in forecast skill induced by the implementation of targeted teleconnections is demonstrated in four applications.