This study presents the benefit of Copula for modeling correlation of tropical sea surface temperature (SST) and hydroclimatic extremes in Serayu river basin, Indonesia. Precipitation and streamflow dataset from ground-based measurement and sea surface temperature dataset from National Oceanic and Atmospheric Administration (NOAA) extending from 1985 to 2017 were analyzed in this study. Principal Component Analysis (PCA) was employed to extract the leading principal components (PCs) that explain more than 50% of data variance. Linear model and Copula model were utilized to detect tropical regions that possess strong correlation with hydroclimatic extremes. Within these regions, Bayesian Dynamic Linear Model (BDLM) was used to scrutinize the role of regional SST in the tropical regions. The result suggests increasing trend of daily hydroclimatic extremes and decreasing tendency of seasonal extremes over the period of 1985–2014, signifying intense floods and droughts in the study area. Furthermore, while typical regions owing to have powerful link with hydroclimatic extremes are detected from linear model, different regions are produced by Copula model. This indicates that the Copula model complements the linear model, partially due to the ability of Copula model in gathering marginal distribution of the joint variables. Additionally, the larger areas spotted from Copula model strengthen the inference of influence of El Nino Southern Oscillation (ENSO) and Indian Oscillation Dipole (IOD) on hydroclimatic extremes in tropical regions, such as the river basin in this study. Moreover, it is found that the association of hydroclimatic extremes and IOD is stronger after 2007 while opposite feature is observed on ENSO. These findings conclude the benefit of Copula on hydroclimatic extremes analysis, and the method should be applicable for other regions.