  El Nino is probably the most influential climate phenomenon on interannual
time scales. It affects the global climate system and is associated with
natural disasters and serious consequences in many aspects of human life.
However, the forecasting of the onset and in particular the magnitude of El
Nino are still not accurate, at least more than half a year in advance. Here,
we introduce a new forecasting index based on network links representing the
similarity of low frequency temporal temperature anomaly variations between
different sites in the El Nino 3.4 region. We find that significant upward
trends and peaks in this index forecast with high accuracy both the onset and
magnitude of El Nino approximately 1 year ahead. The forecasting procedure we
developed improves in particular the prediction of the magnitude of El Nino and
is validated based on several, up to more than a century long, datasets.
