Knowledge of the effects of climate factors on net primary production (NPP) is pivotal to understanding ecosystem processes in the terrestrial carbon cycle. Our goal was to evaluate four different categories of effects (physical, climatic, NDVI, and all effects[global]) as predictors of forest NPP in eastern China. We developed regression models with data from 221 NPP in eastern China and identified the best model with each of the four categories of effects. Models explained a large part of the variability in NPP, ranging from 46.8% in global model to 36.5% in NDVI model. In the most supported global model, winter temperature and sunshine duration negatively affected NPP, while winter precipitation positively affected NPP. Thus, winter climate conditions play an important role in modulating forest NPP of eastern China. Spring temperature had a positive affect on NPP, which was likely because a favorable warm climate in the early growing season promotes forest growth. Forest NPP was also negatively affected by summer and autumn temperatures, possibly because these are related to temperature induced drought stress. In the NDVI model, forest NPP was affected by NDVI in spring (positive), summer (negative) and winter (negative) seasons. Our study provides insight into seasonal effects of climate and NPP of forest in China, as well as useful knowledge for the development of climate-vegetation models.