Accurately predicting gross primary productivity (GPP) is crucial for understanding carbon cycling; however, many studies have predominantly investigated GPP using environmental metrics, overlooking the pivotal role of functional traits as intermediaries between environment and GPP and the predictive potential of GPP. Therefore, this study proposes and employed a three-dimensional ”engine” framework to predict GPP and tested it by leveraging functional traits from 2040 plant communities on the Tibetan Plateau, incorporating environmental factors and the length of the plant growing season. Our results challenged the conventional emphasis that the environment plays a predominant role in predicting GPP dynamics, showing that while the environment exerts a minor direct effect, density traits of leaf and length of plant growing season significantly contributed to GPP predictions. With a prediction accuracy close to 0.90, this study underscores the feasibility of the three-dimensional engine framework in GPP prediction However, incorporating nitrogen-to-phosphorus ratio to the framework diminished the model’s predictive accuracy. Within the stoichiometric dimension alone, the prediction accuracy significantly increased with the number of input traits, indicating a substantial potential for enhancing predictive capability. Our research facilitates the dynamic, continuous, and relatively accurate monitoring of GPP, contributing to a better understanding of carbon cycle dynamics and supporting informed ecosystem planning and management.