Dissolved organic matter (DOM) plays an important role in mediating water quality and ecosystem functions. The theoretical river continuum concept (RCC) has been used to predict the transformation of DOM along longitudinal river networks; however, urbanization can significantly change the amount, sources, composition, and lability of aquatic DOM. Little is known about how urban characteristics affect the predictions of DOM dynamics along the urban river continuum and how these patterns vary seasonally. We tested the effects of hydroclimatic (e.g., temperature), anthropogenic (e.g., land use), and spatial (e.g., stream order) drivers on riverine DOM dynamics in metropolitan Atlanta, Georgia, USA, using seasonal synoptic sampling campaigns (n = 93 streams sampled four times in 2021–2022). The quality and quantity of DOM in urban streams shifted seasonally from a high proportion of terrestrially-derived, aromatic DOM with a high humification degree in early spring (March) to a larger quantity of dissolved organic carbon (DOC) and freshly-produced, microbially-derived humic DOM in summer (July and September). In winter (December), the DOM pool was enriched with small-molecular-weight, proteinaceous DOM. Increased human populations and housing densities were correlated to DOC concentrations with high aromaticity in late summer (September). In contrast, in the early spring, mid-summer, and winter, the human populations and housing densities were negatively correlated with terrestrial DOM but positively correlated with proteinaceous DOM. The DOM variation was mostly controlled by hydroclimatic drivers in summer (28%) and switched to the domination of anthropogenic drivers in winter (26%), however, less than 4% DOM variations was attributed to RCC year around. Altogether, the urban signatures dampened the explanatory power of RCC for DOM spatiotemporal variations as a predictive model in anthropogenically-influenced river continuum. Our study highlighted the importance of incorporating various anthropogenic characteristics and seasonality to better understand spatiotemporal carbon dynamics in an urban river network.