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The aim of this thesis is to understand spatiotemporal temperature variability in southern South America by identifying overarching temperature variability states and their associated synoptic-scale meteorological patterns. Further, the temporal frequency of occurrence of those temperature variability states is investigated as is the role of recurrent low-frequency modes of climate variability (El Niño Southern Oscillation and the Southern Annular Mode) on temperature variability. K-means cluster analysis is used to group all months during the period 1980-2015 into four primary categories for summer and winter separately. Monthly maps of temperature anomalies are provided as input to the k-means algorithm and the resulting temperature variability states are the composites of temperature anomaly maps for months assigned to each cluster, illustrating the primary spatial patterns of temperature variability over Southern South America. Composites of synoptic-scale meteorological patterns (wind, geopotential height, and moisture fields) are calculated for months assigned to each cluster to better diagnose the driving meteorology associated with these patterns of temperature variability. Results show that in summer surface wind direction and geopotential height are robust indicators of temperature variability patterns, while in winter jet stream winds are important for diagnosing equatorward excursions of cold air and poleward excursions of warm air. According to the results the El Niño Southern Oscillation and the Southern Annular Mode exhibit some relationship with temperature variability state frequency, with some states more associated with these two modes than others, however they do not appear to be primary drivers of any of the temperature variability states.