  Abrupt transitions are ubiquitous in the dynamics of complex systems. Finding
precursors, i.e. early indicators of their arrival, is fundamental in many
areas of science ranging from electrical engineering to climate. However,
obtaining warnings of an approaching transition well in advance remains an
elusive task. Here we show that a functional network, constructed from spatial
correlations of the system's time series, experiences a percolation transition
way before the actual system reaches a bifurcation point due to the collective
phenomena leading to the global change. Concepts from percolation theory are
then used to introduce early warning precursors that anticipate the system's
tipping point. We illustrate the generality and versatility of our
percolation-based framework with model systems experiencing different types of
bifurcations and with Sea Surface Temperature time series associated to El Nino
phenomenon.
