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Extreme events are phenomena which occupy the tail-end of a distributions PDF. While atmospheric phenomena are decidedly non-Gaussian, the exact shape of these tails of a distribution are relatively unknown. From stochastic theory, it is noted that tails or extremes may be predicted by the behavior of power-law distribution. While prior research for the empirical search for power-laws has been heavily qualitative in nature, this study aims at the quantitative and statistical fitting and analysis of power-laws across the southeastern United States with respect to daily maximum and minimum temperatures. Utilizing a power-law fitting algorithm, we may fit power-law distributions to the PDFs of atmospheric maximum and minimum temperatures. After statistical analysis, we may note the universal significance of these power-law tails throughout the southeastern United States within regions of non-Gaussianity. Further, we analyze varying behavior of these significant power-laws within the distribution's PDF. From this, we may note and observe the behavior of these extremes events in relation to weather and climatic cycles.