Atmospheric wet deposition of nutrients plays a critical role in the biogeochemical exchange between the atmosphere and terrestrial ecosystems. One component of atmospheric wet deposition is dissolved organic matter (DOM), comprised of both dissolved organic carbon (DOC) and dissolved organic nitrogen (DON), which originate from emission sources such as industrial and agricultural activity, biomass burning, and plant organic compounds. Current monitoring networks primarily analyze inorganic compounds in wet deposition, which leaves a key fraction of wet deposition compounds unreported. In the northern hemisphere, seasonality of DOM wet deposition has been shown to correlate with the growing season, however the role of vegetation phenology and potential plant emissions as a major driver of DOM wet deposition variability remains to be examined. Here we used archived samples (n = 836) from the National Atmospheric Deposition Program (NADP) collected in 2018 to develop a spatially distributed dataset of DOM wet deposition across eastern temperate forests. Monthly average deposition concentrations were combined with NASA Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and Daymet Daily Surface Weather data to examine the relationship between vegetation growth and potential precursor plant emissions to DOM wet deposition at 27 NADP sites. Results suggest that seasonal peaks in DOM wet deposition concentrations are linked to higher temperatures and active vegetation growth (i.e. increased vegetation cover and vegetation index metrics) during summer months. This result highlights the role of biogenic emission-deposition processes to drive the seasonal exchange of organic matter between the biosphere and atmosphere. Investigating the links between terrestrial phenology and DOM wet deposition clarifies the influence of vegetation-based emission sources on the timing and magnitude of DOM wet deposition. This study offers new insight into under-represented organic forms of nitrogen and carbon in deposition and improving our ability to predict bioavailable forms of nutrient inputs to ecosystems.