Methane dynamics within salt marshes are complex because vegetation types, temperature, oscillating water levels, and changes in salinity and redox conditions influence CH4 production and emission. These non-linear and complex interactions among variables affect the traditionally expected functional relationships and present challenges for interpretation and developing process-based models. We employ empirical dynamic modeling (EDM) and convergent cross mapping (CCM) as a novel approach for characterizing seasonal/multiday and diurnal CH4 dynamics by identifying causal variables, lags, and interconnections among multiple biophysical variables within a temperate salt marsh using five years of eddy covariance data. EDM/CCM is a nonparametric approach capable of quantifying the coupling between variables while determining time scales where variable interactions are most relevant. We found that gross primary productivity, tidal creek dissolved oxygen, and temperature were important for seasonal/multiday dynamics (rho=0.73-0.80), while water level was most important for diurnal dynamics during both the growing and dormancy phenoperiods (rho=0.72 and 0.56, respectively). Lags for top causal variables (gross primary productivity, tidal creek dissolved oxygen, temperature, water level) occurred between 1-5 weeks at the seasonal scale and 1-24 hours at the diurnal scale. The EDM had high prediction capabilities for intra-/inter-seasonal patterns and annual CH4 sums but with limitations to represent large infrequent fluxes. Results highlight the importance of non-linearity, causal drivers, lag times, and interconnections among multiple biophysical variables that regulate CH4 fluxes in tidal wetlands. This study presents a new dimension for analyzing CH4 fluxes, which will prove helpful to test current paradigms in wetlands and other ecosystems.