We estimate the potential predictability of European winter temperature using factors based on physical studies of their influences on European winter climate. These influences include sea surface temperature patterns in different oceans, major tropical volcanoes, the quasi-biennial oscillation in the tropical stratosphere, and anthropogenic climate change.

We assess the predictive skill for winter mean temperature in northern Europe by evaluating statistical hindcasts made using multiple regression models of temperature for Europe for winter and the January–February season in cross-validated mode. We extend the methodology to all of Europe on a 5o×5o grid and include rainfall for completeness. These results can form the basis of practical prediction methods. However, our main aim is to develop ideas to act as a benchmark for improving the performance of dynamical climate models. Because we consider only potential predictability, many of the predictors that we use have estimated values coincident with observations the winter season being forecast. However, in each case, such values are predictable on average with considerable skill in advance of the winter season.

A key conclusion is that dynamical forecasting models will require a fully resolved stratosphere. Furthermore, ongoing research indicates that additional sources of European winter climate predictability are emerging. These may come via solar ultraviolet radiation forcing of the stratosphere and forcing from Arctic sea ice extent variations and changes, the latter partly due to anthropogenic warming. Again, dynamical seasonal forecasting models will need realistic responses to these effects. This may require high resolution horizontally, as well as high resolution in the stratosphere.