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Climate warming is expected to influence forest growth, composition and distribution. However, accurately estimating and predicting forest biomass, potential productivity or forest growth is still a challenge for forest managers dealing with land-use at the stand to regional levels. In the present study, we predicted the potential productivity (PP) of forest under current and future climate scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) in Jilin province, northeastern China by using Paterson’s Climate Vegetation and Productivity (CVP) index model. The PP was validated by comparing it with the mean and maximum net primary production calculated from light energy utilization (GLM_PEM). Our results indicated that using the CVP index model is partially valid for predicting the potential forest productivity in northeastern China. PP exhibited obvious spatial heterogeneity varying from 4.6 to 8.9 m 3  ha −1  year −1 with an increasing tendency from northwest to southeast driven by the precipitation across the region. The number of vegetation-active months, precipitation and insolation coefficient were identified as the primary factors affecting PP, but no significant relationship was found for warmest temperature or temperature fluctuation. Under future climate scenarios, PP across the Jilin Province is expected to increase from 1.38% (RCP2.6 in 2050) to 15.30% (RCP8.5 in 2070), especially in the eastern Songnen Plain (SE) for the RCP8.5 scenarios.

Impacts of climate change on the potential forest productivity based on a climate-driven biophysical model in northeastern China