Elevated atmospheric CO2 (eCO2) influences the carbon assimilation rate and stomatal conductance of plants, and thereby can affect the global cycles of carbon and water. However, the extent to which these physiological effects of eCO2 influence the land-atmosphere exchange of carbon and water is uncertain. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We use a comprehensive process-based land surface model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) to simulate the leaf-level effects of increasing atmospheric CO2 concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO2 effects. The signal in gross primary production (GPP) emerges at relatively low eCO2 (Δ[CO2] ~ 20 ppm) where the leaf area index (LAI) is relatively high. Compared to GPP, the eCO2 effect causing reduced 28 transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (Δ[CO2] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO2 is detectable earlier for variables of the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass- dominated ecosystems. Our results provide a step towards when and where we expect to detect physiological CO2 effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.