## 2 Materials and Methods
### Climate Scenarios  
In this study, we used four representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.5, and RCP8.5) to determine whether _Pinus densiflora_ would help curb climate change through the process of capturing atmospheric CO\({}_{2}\). To investigate its potential effects on climate change mitigation, seasonal changes in CO\({}_{2}\), precipitation, and maximum and minimum temperature were considered (Table 1). We explored the growth and climate sensitivity of _Pinus densiflora_ under baseline (1986 to 2015) and climate change conditions (the 2050s: 2036 to 2065). These 30-year periods were chosen to minimize seasonal and interannual weather variability. The seasonal climate change scenarios were estimated from the average values of 48 general circulation model (GCM) ensembles using KNMI Climate Explorer ([[https://climexp.knmi.nl](https://climexp.knmi.nl)]([https://climexp.knmi.nl](https://climexp.knmi.nl))). More details can be found in KNMI Climate Explorer. Four different atmospheric CO\({}_{2}\) concentrations were used for RCP2.6, RCP4.5, RCP6.5, and RCP8.5, which were 440, 480, 485, and 550 ppm, respectively. The baseline was evaluated at the present concentration of CO\({}_{2}\) (400 ppm). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Season**} & \multirow{2}{*}{**CO\({}_{2}\) (ppm)**} & \multirow{2}{*}{**PPT (\%)**} & **Min Tem** & **Max Temp** \\  & & & & (\({}^{\circ}\)**C**) & (\({}^{\circ}\)**C**) \\ \hline \multirow{4}{*}{_RCP2.6_} & Dec–Feb & & 1.04 & & \\  & Mar–May & & 1.06 & 1.20 & 1.22 \\  & Jun–Aug & & 1.04 & 1.08 & 1.14 \\  & Sep–Nov & & 1.03 & 1.21 & 1.28 \\  & Dec-Feb & & & 1.04 & \\  & Mar–May & & 1.06 & 1.59 & 1.60 \\  & Jun–Aug & & 1.06 & 1.45 & 1.45 \\  & Sep–Nov & & 1.04 & 1.57 & 1.59 \\  & & & & 1.60 & 1.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Climate change scenarios. Comparisons of seasonal changes in CO\({}_{2}\), precipitation (PPT), and minimum (Min) and maximum (Max) temperature (Temp) were considered under four different RCP pathways (RCP2.6, RCP4.5, RCP6.5, and RCP8.5) between the baseline in the 2010s (from 1986 to 2015) and 2050s (2036 to 2065). These data were obtained from KNMI Climate Explorer ([[https://climexp.knmi.nl](https://climexp.knmi.nl)]([https://climexp.knmi.nl](https://climexp.knmi.nl))). To achieve statistical confidence in the results of _Pinus densiflora_ growth projections under climate change, we simulated _ecosys_ for 50 years using a weather generator [39], which was used to reproduce a set of hourly meteorological variables including precipitation, temperature, solar radiation, relative humidity, wind speed, atmospheric pressure, and cloud cover (Figure 1). The inputs of the weather generator, except for the listed weather variables, included latitude, longitude, hours of difference from Greenwich Mean Time, and elevation. We used observed weather data from 1986 to 2015 to estimate the parameters of the weather generator. For the baseline scenario, the reproduced weather data were implemented to determine the effect of interannual weather variations in _Pinus densiflora_ growth projections. For the climate change scenario, changes in CO\({}_{2}\), precipitation, and minimum and maximum temperature were applied to the baseline scenario. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Season** & **CO\({}_{2}\) (ppm)** & **PPT (\%)** & **Min Temp** & **Max Temp** \\  & Dec–Feb & & 1.01 & & \\  & Mar–May & 1.05 & 1.38 & 1.40 \\ _RCP6.0_ & Jun–Aug & & 1.03 & 1.19 & 1.20 \\  & Sep–Nov & & 1.05 & 1.18 & 1.23 \\  & Dec–Feb & & 1.07 & & \\  & Mar–May & 1.09 & 2.17 & 2.23 \\ _RCP8.5_ & Jun–Aug & & 1.07 & 1.97 & 1.98 \\  & Sep–Nov & & 1.06 & 2.10 & 2.13 \\  & & & 2.22 & 2.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Observed (green) and reproduced (black) weather forcing data.