We used a simple, systematic data-analytics approach to determine the relative linkages of different climate and environmental variables with the canopy-level, half-hourly CO2 fluxes of US deciduous forests. Multi- variate pattern recognition techniques of principal com- ponent and factor analyses were utilized to classify and group climatic, environmental, and ecological variables based on their similarity as drivers, examining their inter- relation patterns at different sites. Explanatory partial least squares regression models were developed to estimate the relative linkages of CO2 fluxes with the climatic and environmental variables. Three biophysical process com- ponents adequately described the system-data variances. The ‘radiation-energy’ component had the strongest link- age with CO2 fluxes, whereas the ‘aerodynamic’ and ‘temperature-hydrology’ components were low to moder- ately linked with the carbon fluxes. On average, the ‘radiation-energy’ component showed 5 and 8 times stronger carbon flux linkages than that of the ‘temperature- hydrology’ and ‘aerodynamic’ components, respectively. The similarity of observed patterns among different study sites (representing gradients in climate, canopy heights and soil-formations) indicates that the findings are potentially transferable to other deciduous forests. The similarities also highlight the scope of developing parsimonious data-driven models to predict the potential sequestration of ecosystem carbon under a changing climate and environment. The presented data-analytics provides an objective, empirical foundation to obtain crucial mechanistic insights; com- plementing process-based model building with a warranted complexity. Model efficiency and accuracy (R2 = 0.55– 0.81; ratio of root-mean-square error to the observed standard deviations, RSR = 0.44–0.67) reiterate the use- fulness of multivariate analytics models for gap-filling of instantaneous flux data.