## 2 Study Area and Data
### Data
#### 2.2.2 Climate Variables  
In this study, the Tibetan Plateau Meteorological Forcing Dataset (TPMFD) was used to train the CNN model for the historical period of 1982-2013. Developed by [PERSON] et al. (2018), the TPMFD integrates high-resolution WRF simulations, long-term ERA5 data ([PERSON] et al., 2019), and station observations using a deep learning model. Spanning from 1979 to 2020, this dataset offers high-spatial-resolution (\(1/30^{\circ}\)) meteorological data for the Tibetan Plateau at monthly, daily, and hourly intervals. Six climate variables--precipitation (prcp), temperature (temp), specific humidity (shum), wind speed (wind), longwave radiation (lard), and shortwave radiation (srad)--were selected for this study as they are critical drivers of plant photosynthesis and ecosystem productivity. These variables directly influence key physiological processes in plants, such as photosynthesis and transpiration, while also playing a significant role in regulating the energy balance within ecosystems. Notably, prcp, temp, and shum are derived from the fusion of short-term WRF simulations with long-term ERA5 data and station observations, providing higher precision compared to conventional reanalysis datasets. The TPMFD is a crucial resource for climate analysis, land surface studies, and ecological modeling in the Third Pole region. The dataset is publicly available at [[https://data.tpdc.ac.cn/zh-hans/data/44a449](https://data.tpdc.ac.cn/zh-hans/data/44a449) ce-e660-44c3-bbf2-31 ef7d716 ec7]([https://data.tpdc.ac.cn/zh-hans/data/44a449](https://data.tpdc.ac.cn/zh-hans/data/44a449) ce-e660-44c3-bbf2-31 ef7d716 ec7) (accessed on 30 September 2024). In this study, climate variables from the NEX-GDDP-CMIP6 dataset were used to predict the future GPP under four SSP scenarios: ssp126, ssp245, ssp370, and ssp585 ([PERSON] et al., 2019; [PERSON] et al., 2019). These scenarios represent different socioeconomic pathways, ranging from sustainable development (ssp126) and middle-of-the-road growth (ssp245) to uneven regional development (ssp370) and fossil-fuel-intensive futures (ssp585) ([PERSON] et al., 2019). The NEX-GDDP-CMIP6 dataset provides downscaled climate projections at a 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial resolution, offering detailed insights into future environmental changes ([PERSON] et al., 2019). This study used projected data from 2021 to 2100, derived from seven ESMs under these SSP scenarios, as outlined in Table 1, which can be downloaded from [[https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html#NEX-GDDP-CMIP6/](https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html#NEX-GDDP-CMIP6/)]([https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html#NEX-GDDP-CMIP6/](https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html#NEX-GDDP-CMIP6/)) (accessed on 30 September 2024). The climate variables selected--precipitation (prcp), temperature (temp), specific humidity (shum), wind speed (wind), longwave radiation (lard), and shortwave radiation (srad)--are critical in understanding vegetation growth and GPP dynamics, influencing spatial and temporal variations in primary productivity ([PERSON] et al., 2019). These variables were therefore chosen to forecast the future GPP.