## 4 Discussion
### Limitations and Outlook of This Study  
The NDVI shows varying degrees of lag in response to different climate factors for different types of vegetation, which is crucial for analyzing the climate-vegetation relationship. [PERSON] et al. pointed out that there are significant differences in the responses of various vegetation types and growth stages to climate factors. NDVI responds more significantly to temperature than other factors, and the lag time varies greatly depending on the growth  
Figure 8: Spatial distribution of changes in various meteorological factors and NDVI in China under the SSP585 scenario from 2023 to 2100: panels (**a–f**) show the spatial distribution of changes in SWVL1, RH, TP, SWR, T2m, and NDVI, respectively. stage and vegetation type. Precipitation typically has a lag of 1 to 2 months on NDVI, with precipitation in May and June having the greatest impact on NDVI for alpine grasslands, alpine sparse vegetation, and temperate desert vegetation types [102]. [PERSON] et al. pointed out that the lag effect is a key factor in studying vegetation's response to climate anomalies. In semi-arid regions, grasslands have the shortest lag time in response, possibly because grasslands are more susceptible to water stress than other vegetation types associated with crops. In contrast, deciduous forests have the longest lag time in response, which means that when deciduous forests face extreme climate conditions, their own stress response and adaptive capacity make them less affected by climate anomalies [103]. [PERSON] et al. pointed out that vegetation growth is significantly influenced by the lag effect of climate factors. The proportion of grids across the entire Tibetan Plateau affected by the combined lag and cumulative effects of climate factors ranges from 43.42% to 45.78%. When establishing models to relate climate factors and vegetation indices, the combined lag-cumulative effects of climate factors should be fully considered. Multiple studies have shown that analyzing these lag effects can help us more accurately predict how different types of vegetation will respond to climate change [104]. However, this study is primarily based on an annual scale, and therefore, it does not cover the impact of lag effects on the interaction between vegetation and climate. However, it is important to note that this study primarily relies on data collected from 2001 to 2023 due to the temporal limitations of the MODIS dataset. Although this period provides valuable insights into the short-term dynamics between climate and vegetation, it does not reach the 30-year benchmark recommended by the World Meteorological Organization (WMO) for robust climate cycle analysis [105]. This limitation may affect our ability to capture long-term trends and variability in the interactions between climate factors and vegetation health. In addition, the predictive model developed in this study may have uncertainties in long-term forecasting. When using CMIP6 climate scenario data for future NDVI prediction, the inherent uncertainties of CMIP6 climate models present a challenge, potentially leading to uncertainties in vegetation's response to climate change. At the same time, the relationship between climate factors (such as temperature and precipitation) and vegetation response (NDVI) may change over time, which could lead to biased NDVI predictions when climate or ecological processes undergo significant changes. Moreover, this study acknowledges certain limitations related to data quality and retrieval processes. First, the datasets used in this study--particularly the meteorological and remote sensing data--have inherent uncertainties. For example, spatial and temporal mismatches between climate variables and NDVI observations may affect the accuracy of the derived relationships. Second, although the MODIS NDVI product undergoes rigorous cloud screening, thin clouds may still influence pixel values, potentially introducing noise or bias into the NDVI time series.