Technical Abstract: Net Primary Production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5,500 global data points with observed mean annual NPP, land cover class, precipitation and temperature were compiled. Precipitation was better correlated with NPP than temperature and it explained much more of the variability in mean annual NPP for grass or shrub dominated systems (r2=0.68) than tree dominated systems (r2=0.39). For a given precipitation level, tree dominated systems had significantly higher NPP (~100-150 gC m-2 yr-1) than non-tree dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree dominated systems. Our new NCEAS model predicts NPP for tree dominated systems based on precipitation and temperature, but for non-tree dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree dominated systems is not entirely explained by decreased water or nutrient use efficiency but is related to more frequent fire disturbances and higher nutrient loss rates. Late 20th century above ground and total NPP for potential native vegetation using the NCEAS model is estimated to be ~28 and ~46 Pg C yr-1, respectively. The NCEAS model estimated a ~13% increase in global TNPP for potential vegetation from 1901-2000 based on changing precipitation and temperature patterns.