Ecosystem CO2 fluxes measured with eddy-covariance techniques provide a new opportunity to retest functional responses of photosynthesis to abiotic factors at the ecosystem level, but examining the effects of one factor (e.g., temperature) on photosynthesis remains a challenge as other factors may confound under circumstances of natural experiments. In this study, we developed a data mining framework to analyze a set of ecosystem CO2 fluxes measured from three eddy-covariance towers, plus a suite of abiotic variables (e.g., temperature, solar radiation, air, and soil moisture) measured simultaneously, in a Californian oak-grass savanna from 2000 to 2015. Natural covariations of temperature and other factors caused remarkable confounding effects in two particular conditions: lower light intensity at lower temperatures and drier air and soil at higher temperatures. But such confounding effects may cancel out. At the ecosystem level, photosynthetic responses to temperature did follow a quadratic function on average. The optimum value of photosynthesis occurred within a narrow temperature range (i.e., optimum temperature, T opt): 20.6 ± 0.6, 18.5 ± 0.7, 19.2 ± 0.5, and 19.0 ± 0.6 °C for the oak canopy, understory grassland, entire savanna, and open grassland, respectively. This paradigm confirms that photosynthesis response to ambient temperature changes is a functional relationship consistent across leaf–canopy–ecosystem scales. Nevertheless, T opt can shift with variations in light intensity, air dryness, or soil moisture. These findings will pave the way to a direct determination of thermal optima and limits of ecosystem photosynthesis, which can in turn provide a rich resource for baseline thresholds and dynamic response functions required for predicting global carbon balance and geographic shifts of vegetative communities in response to climate change.