Though the importance of Earth's internal climate modes such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) to regional-scale climate variability is well recognized, the degree to which these oscillations are reflected by spatio-temporal salinity variability over interannual timescales in estuaries is less understood. Here an 11-year continuous salinity monitoring dataset spanning 223 stations across Louisiana's coastal wetlands along the northern Gulf of Mexico is examined with empirical orthogonal function (EOF) analysis to identify dominant modes of interannual variability in the salinity field. The first EOF mode accounts for 72% of the variance in the salinity field and captures a domain-wide pattern where salinities vary in-phase through space in response to local precipitation anomalies occurring in the vicinity of the study area. This local precipitation anomaly is positively correlated with ENSO (Nino3.4 index), consistent with the El Niño – wet (La Niña – dry) precipitation teleconnection that is prevalent throughout the northern Gulf of Mexico coast. The second EOF mode, which accounts for 13% of the variance in the salinity field, is expressed primarily in the marshes across the lower reaches of the Mississippi River deltaic plain (MRDP). EOF2 is anticorrelated with annual Mississippi River discharge anomaly such that salinities in the lower MRDP decrease as discharge increases, pointing to enhanced advection of fresh river plume waters over the shelf into the estuary via estuary-ocean exchange during years of anomalously high river discharge. Mississippi River discharge anomaly is positively correlated with the NAO at a one-year time lag, through a teleconnection with precipitation throughout much of the central region of the Mississippi River drainage basin. Together, these findings indicate that most of the interannual salinity variability across Louisiana's coastal wetlands can be linked to climate variability through teleconnections with precipitation. Incorporating these dynamics into restoration planning, monitoring, and adaptive management efforts may help constrain background environmental variation and better isolate restoration effects.