Rainwater is not only a critical source of drinking and agricultural water but it plays a key role in the fate and transport of contaminants through their removal by wet deposition. Rainwater is a complex mixture of organic compounds yet despite its importance its spatial and temporal variability are not well understood and less than 50% of the organic matter has been characterized. In-depth analytical approaches were used in this study to characterize the seasonal variation in rainwater composition. Rainwater samples were collected over a one-year period in Scarborough, Ontario, Canada. The seasonal variation of atmospheric organic carbon (AOC) in rainwater was analyzed by excitation-emission matrix spectroscopy (EEMs), 1D and 2D NMR with compound identification by spectral database matching, GC-MS, FT-ICR-MS, and GC×GC-TOFMS. This combination of techniques provided four complementary datasets, with less than 10% overlap, of anthropogenic and biogenic AOC. NMR with database matching identified over 100 compounds, primarily carboxylic acids, carbohydrates, and nitrogen-containing compounds. GC×GC-TOFMS analysis identified 344 compounds in two rain events with 33% of the compounds common to both events. FT-ICR-MS generated a seasonally dependent profile of 1226-1575 molecular ions of CHO, CHOS, and CHON elemental composition. FT-ICR-MS and GC×GC-TOFMS datasets were compared using van Krevelen diagrams (H/C vs. O/C), the H/C ratio vs. mass/charge (m/z), and the carbon oxidation state/carbon number matrix. Fluorescence patterns were correlated with NMR results resulting in the identification one seasonally-dependent component of chromophoric dissolved organic matter (CDOM). This study demonstrated the importance of using of an integrated analytical approach to monitor the compositional variation of AOC.