Abstract: The El Niño Southern Oscillation (ENSO) is the most prominent phenomenon of climate variability and affects weather and climate in large parts of the world. A particular challenge in its forecasting is the spring predictability barrier, which hinders skillful forecasts before or during the boreal spring. Recently, we have developed a dynamical network approach for predicting the start of El Niño events well before the spring barrier. In the climate network, the nodes are grid points in the Pacific, and the strengths of the links (teleconnections) between them are characterized by the crosscorrelations of the atmospheric surface temperatures at the grid points. In the year before an El Niño event, the links between the eastern equatorial Pacific and the rest of the Pacific tend to strengthen such that the mean link strength exceeds a certain threshold. This feature allows predicting an El Niño event in the following year with 80% probability. In order to see if this predictive feature is also present in GCMs we analyzed their output and found that the models lack this predictive power.