Storms can drive massive fluxes of carbon from land to ocean via rivers, contributing a large but relatively uncertain term to coastal carbon budgets. To estimate storm-driven aquatic organic carbon fluxes and develop a framework for future observations, we conducted a case study of extreme precipitation followed by river discharge to the Chesapeake Bay watershed, on the east coast of the United States over two decades from June 2001 to July 2021. Anomalous high precipitation events were defined as the top 5% of five-day accumulated precipitation over the watershed and we identified 80 distinct large storms over the Chesapeake Bay region. The top 1% of five-day accumulated river discharge from the United States Geological Survey (USGS) gage stations provided secondary storm criteria to distinguish extreme precipitation that manifest in very high flow: over the same period, we identified 16 storms that led to very high streamflow and combined streamflow with observations of POC and DOC to estimate daily mass flux from the seven major rivers. An extremely large convective storm and subsequent river discharge in July 2018 was studied to determine the association between precipitation, river discharge, and satellite ocean color in combination with in situ observations. MODIS-Aqua satellite derived dissolved organic carbon (DOC) concentration was calculated for all images in 2004–2021 and the estimated DOC values in 2018 were used to assess the feasibility of the optical satellites to monitor coastal storm fluxes of carbon. Using the USGS Load Estimator stream flow model and historical observations, the 2018 event exported approximately 12.9 Gg C particulate organic carbon (POC) and 12.5 Gg C DOC into the bay for 7.2% and 4.4% of the annual total, respectively. The largest storm over the last 20 years exported up to 16% of the annual total POC and 11% of the annual total of DOC from the major rivers over 10 days. However, monitoring the evolution of riverine carbon in estuarine environments from optical satellites alone remains challenging due to cloud cover, thus additional observing platforms assimilated through models are essential.