This study introduces a copula-based tool designed to assess the joint exceedance probabilities of compounding inland and coastal hydrologic processes resulting in extreme water levels. Understanding the dependence structure of the flood drivers is of great importance for the adequate design of water infrastructure in coastal regions. The developed tool combines conditional sampling and copula theory to derive joint exceedance probabilities of input variables. The methodology was tested through a pilot application to a North Florida location. Due to a lack of direct surface water and groundwater observations, extreme precipitation recorded at a nearby rain gauge was selected to represent the occurrence of extreme inland flow conditions. The validity of this selection was tested by comparing the basin-averaged precipitation against the observations at the selected rain gauge. Precipitation records and coastal water level observations at a NOAA tidal station were used to represent the dependence structure of the inland and coastal processes. The results of the copula-based analysis were compared against traditional bivariate frequency analysis methods. By integrating advanced statistical techniques into an accessible online platform, this tool is expected to advance our understanding of compound flood risk in coastal areas.