Understanding the interannual variability of forest ecosystem carbon fluxes and its driving factors is important for understanding dynamic changes in forest carbon budgets and predicting the effects of future climate change on forest carbon budgets. In addition, it is a critical part of assessments of the contribution of forests to climate change mitigation. Using the MODIS Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), MERRA reanalysis data, and flux tower observation data, the Eddy Covariance Light Use Efficiency (EC-LUE) model was calibrated and validated to estimate the spatial distribution of the Gross Primary Productivity (GPP) of the Moso bamboo (Phyllostachys edulis) forest in Anji County during 2004-2011. Then, the relationships between environmental and biological factors and the interannual variability of the GPP were analyzed, in order to identify the factors driving the interannual variability of the GPP. Our results show that: (1) the GPP is slightly greater in off-years (between mass flowering events) than that in on-years (years with mass flowering events). The reasons for this are as follows: the leaves found on the culms during off-years are one year old, and thus have higher photosynthetic capacities than older leaves. The leaves of adult bamboo plants stay green in off-year winters and produce large quantities of photosynthates, which support the growth of new bamboo shoots through the rhizome system in the following year (an on-year); (2) the annual average daily GPP decreased from 2004 to 2011, at rates of -0.064, -0.033, and -0.045g C m^-2 d^-1 per year in the eastern portion, western portion, and the entirety of Anji County, respectively. Annual average temperature was the main factor driving decreases in the annual average daily GPP; (3) interannual variability in the LAI is the main driving factor of interannual variability in the GPP, because the effective LAI changed noticeably from on-years to off-years; and (4) the interannual variability of the GPP in the western region was greater than that in the eastern region of Anji County, with coefficients of variation of 70% and 54%, respectively. The primary reason for this is that the effects of environmental factors on the interannual variability of the GPP are positively correlated with the effects of biological factors on the interannual variability of the GPP in the western region, so increases in both the environmental and biological factors increase the interannual variability of the GPP. In the eastern region, the effects of environmental factors on the interannual variability of the GPP are negatively correlated with the effects of biological factors on the interannual variability of the GPP, and thus both the environmental and biological factors decrease the interannual variability of the GPP in the east. Therefore, the interannual variability of the GPP is determined by both environmental and biological factors.