The exponential growth of climate data combined with advances in machine learningoffers new opportunities to understand the climate system and its response to external forcings. This thesis explores and proposes data mining frameworks to reduce the complexityof spatiotemporal climate fields and facilitate analysis and interpretation.As complex as it appears, the dynamics of the climate system is dominated by spatiotemporal patterns and the identification of these patterns and their linkages offers a useful framework for dimensionality reduction.