Predicting climate events means having to deal with a complex, nonlinear, chaotic system, with natural variability in time and space and subject to external forcing. One of the best ways to approach this problem is through the definition of general circulation models, which represent climate systems as a group of subsystems that interact with each other through phenomena. However, they are not always simple to interpret, and sometimes it is necessary to have easy models to read but precise enough to be able to hold all the relevant information. Empirical model reduction (EMR) is a well-established methodology able to build an efficient model from simple observations of the system.