## Materials and Methods
### Input Data  
The LPJ model was driven by meteorological data, annual CO\({}_{2}\) data, and soil texture data. Monthly fields of mean meteorological data, including monthly mean air temperature, precipitation, cloud cover, and the number of wet days at a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) from 1901 to 2016, were taken from the Climate Research Unit (CRU), University of East Anglia, called CRU TS3.25 [51; 52]. The annual historical global atmospheric CO\({}_{2}\) data from 1901 to 1958 were obtained from the Carbon Cycle Model Linkage Project [53; 54], which was extended to 2016 using the atmospheric CO\({}_{2}\) data from the Mauna Loa observatory. The soil texture data were retrieved from the FAO soil data set [55; 56; 57]. This study also used outputs from two climate models (National Center for Atmospheric Research-Community Climate System Model (NCAR-CCSM) and the National Centre for Meteorological Research-Coupled Model (CNRM-CN)) under four IPCC SRES emission scenarios(AR4 A2 & B1 scenarios and AR5 RCP45 & RCP85 scenarios) to investigate the impacts of future climate and CO\({}_{2}\) changes on NPP of terrestrial ecosystems in China. Eight future climate scenarios (Table 1) from CMIP3 and CMIP5 (Coupled Model Intercomparison Project Phase 5) were used to generate the future climates of 2017-2100. The monthly fields of mean temperature, precipitation, and cloud cover in CMIP3 and CMIP5 during 2001-2100 were downloaded from the World Data Center for Climate ([[https://cera-www.dkrz.de/WDCC/ui/cerasearch/](https://cera-www.dkrz.de/WDCC/ui/cerasearch/)]([https://cera-www.dkrz.de/WDCC/ui/cerasearch/](https://cera-www.dkrz.de/WDCC/ui/cerasearch/))) and the LASG ([[http://www.lasg.ac.cn/](http://www.lasg.ac.cn/)]([http://www.lasg.ac.cn/](http://www.lasg.ac.cn/))), respectively. All the data were interpolated into 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) grids. The monthly wet days from 2001 to 2100 were calculated with projected precipitation and historical averages of monthly precipitation and wet days [51] as follows:  
\[wet=\left(\frac{pre*\overline{wet}^{\frac{1}{0.45}}}{\overline{pre}}\right)^{0. 45} \tag{1}\]  
where pre is the monthly precipitation in the future, \(\overline{pre}\) is the historical mean monthly precipitation, and \(\overline{wet}\) is the historical mean number of monthly wet days. To adjust for climate model bias, the additive departures of projected temperature and multiplicative departures of projected precipitation were calculated to produce time series of the future climates for each grid during 2017-2100 [22]. Departures of projected temperature and precipitation were estimated according to anomalies of projected climates in CMIP3 during 2001-2016 and CMIP5 during 2006-2016 relative to historical climates from CRU TS3.25 during 2001-2016. The time series of the mean annual temperature (MAT) and mean annual precipitation (MAP) over China are shown in Figure 1. Overall, the MAT in China obviously increases, while the MAP has no clear tendency. The MAT in China changed from 6.36 \({}^{\circ}\)C to 8.24 \({}^{\circ}\)C during 1961-2016, with an average of \(7.21\pm 0.48\)\({}^{\circ}\)C and an increase rate of 0.024 \({}^{\circ}\)C\(\cdot\)yr\({}^{-1}\). The increasing rates (average of NCAR and CNRM outputs) of the MAT under the A2 and RCP85 scenarios were 0.053 \({}^{\circ}\)C\(\cdot\)yr\({}^{-1}\) and 0.054 \({}^{\circ}\)C\(\cdot\)yr\({}^{-1}\) during 2017-2100, respectively, which were significantly larger than those under the B1 (0.013 \({}^{\circ}\)C yr\({}^{-1}\)) and RCP45 (0.022 \({}^{\circ}\)C yr\({}^{-1}\)) scenarios. The MAP in China changed from 563 mm to 721 mm during 1961-2016, with an average of \(631\pm 32\)mm. There were large interannual fluctuations in MAP during 2017-2100 in the different models, especially under the AR4 scenario.