Massive climate simulation datasets are produced due to the unprecedented increase in computing power, and there is a need to provide automated methods for analyzing these data. We describe an automated method for the identification of the extreme events in large sets of climate simulation data. This method adapts an algorithm for topological data analysis to extract numerical features of topological descriptors called connected components. The features are then fed to a supervised machine learning classifier. The classifier performs a binary classification task to identify the extreme weather patterns we are interested in.