Plants play an essential role in terrestrial ecosystems by contributing significantly to productivity, including gross (GPP) and net primary productivity (NPP), which form the basis of material cycle and energy flow in ecosystems. Therefore, improving the accuracy of models that predict ecosystem productivity is important in ecology. Previous studies have primarily used ecological process models on a large-leaf model to predict the NPP spatiotemporal variation. This approach simulates NPP using the unity principle considering the photosynthesis and respiration mechanistic processes, and it can predict spatiotemporal variation in the NPP at the macroscale under a scenario driven by environmental variables. The prediction of spatiotemporal variation in NPP is highly uncertain owing to the limitations of excessive scaling up between test and prediction and the difficulty in accurately obtaining these parameters at the regional or global levels. The regulation of primary productivity using plant functional traits has attracted extensive attention from researchers, and correlations between ecosystem productivity and specific leaf functional traits have been observed in previous studies. However, the underlying mechanisms in which diverse plant functional traits drive various ecosystem functions are unclear. Although previous studies have attempted to use plant functional traits as input parameters in dynamic vegetation models, prediction accuracy falls short of expectations. Previous studies have referenced the output of the classical engine power model and combined it with the two-dimensionality of plant community traits to develop a novel trait-based productivity (TBP) framework. In this chapter, the theoretical basis of the TBP framework and two potential methods of realization are comprehensively discussed. Furthermore, four examples of the application of the novel TBP framework are discussed: (1) using the nitrogen community trait to predict the NPP spatial pattern and temporal dynamics in typical Chinese ecosystems, (2) the leaf chlorophyll community trait to explore the productivity spatial variation and its main influencing factors in Northern China grassland areas, (3) several community traits to predict the spatial variations in ecosystem productivity in China, and (4) several community traits to demonstrate the spatial variation of ecosystem multifunctionality in China. Furthermore, the TBP framework is based on the two-dimensionality of plant community traits standardized per unit land area. This framework can be integrated with the rapid development of remote sensing and hyperspectral and flux observations, and other advanced technologies to improve our predictive capabilities regarding spatiotemporal variations in ecosystem productivity and multiple functionalities, providing a basis for the development of a new generation of process models in the future.