<p>The probabilistic analysis of drought events is a crucial scientific process that provides foundational data for developing water resource strategies to ensure water supply for municipal, industrial, and agricultural purposes. Drought analysis requires the consideration of two variables–duration and severity–making it more complex than flood frequency analysis, which typically involves a univariate analysis. In the bivariate analysis of drought events, the derivation of a joint probability distribution using the best-fit probability distributions for the selected variables was difficult or impossible mathematically. Therefore, recent studies have applied a copula function to resolve this limitation. Although recent research has focused on applying copula functions, comparative studies presenting results from univariate analyses, bivariate analyses using specific distributions, and bivariate analyses using copula functions remain relatively scarce. Therefore, this study attempts to focus on the comparison of the results from techniques used in drought frequency analysis and to suggest the advantage of a copula function. The sites selected for this study were Hongcheon and Jeongseon in South Korea, which experienced severe drought damage in 2009. In addition, six rainfall datasets (historical data and future data by SSP1-2.6, and SSP5-8.5 climate change scenarios) from two rainfall gauges were used to perform various types of drought frequency analyses. In particular, the fundamental theory that considers the relationship between the return period and exceedance probability in the bivariate analysis suggests that copula functions can effectively enhance drought frequency analysis.</p>