  The skill of current predictions of the warm phase of the El Ni\~no Southern
Oscillation (ENSO) reduces significantly beyond a lag of six months. In this
paper, we aim to increase this prediction skill at lags up to one year. The new
method to do so combines a classical Autoregressive Integrated Moving Average
technique with a modern machine learning approach (through an Artificial Neural
Network). The attributes in such a neural network are derived from topological
properties of Climate Networks and are tested on both a Zebiak-Cane-type model
and observations. For predictions up to six months ahead, the results of the
hybrid model give a better skill than the CFSv2 ensemble prediction by the
National Centers for Environmental Prediction (NCEP). Moreover, results for a
twelve month lead time prediction have a similar skill as the shorter lead time
predictions.
