**5. Conclusions**

Using remote-sensing-based NDVI, several sets of reanalysis, and meteorological station observed data, this paper explored the influence of the ISM on the NDVI interannual variability on the TP in JJAS. The findings reveal that the ISM is an external factor affecting the inter-annual variation of TP vegetation in the growing season. Furthermore, the contribution of ISM is more direct and significant than that of ENSO and IOBM. The ISM, TP NDVI, and TP precipitation are all positively correlated with each other. Although precipitation is a direct factor of vegetation growth, the correlation between precipitation and NDVI greatly decreases after removing their linear trends and is much smaller than that between the ISM and NDVI. This implies that the ISM influences the TP vegetation not only by changing precipitation but also by inducing the changes in thermal factors in the TP.

Corresponding to a strong ISM, anticyclonic circulation develops over the TP in the upper troposphere, and significant cyclonic circulation develops over the southern TP in the middle-lower troposphere, which also represents a strengthened SAH. This upper and middle-lower tropospheric circulation structure enhances upward motion over the TP. Moreover, the middle-lower tropospheric cyclones can induce more water vapor to the south of the TP. The sufficient water vapor and strengthened upward motion both facilitate more precipitation over the southwest of the TP, which affects vegetation growth. The ISM-induced increase in precipitation over the TP also affects the TP thermal conditions by modulating sunshine duration. Moreover, vegetation can affect TP thermal conditions through its evapotranspiration and coverage. The increased vegetation causes the TP warming, and the TP warming can in turn promote vegetation growth. Further multiple regression analysis revealed that the ISM and its induced changes in local climatic factors can account for more than 52% of the NDVI inter-annual variability on the TP in JJAS.

Additionally, changes in TP thermal conditions, which are regulated by the NDVI in late summer and early autumn, may influence the relationship between the ISM and TP vegetation. In the early growing season (June–July), the UNPI and ISM are significantly correlated. Composite analysis suggests that the TP NDVI causes the changes in TP thermal conditions and thus affects the ISM intensity. Relative to June–July, the ISM intensity is weaker in August–September. This weakening will be more severe in the case of increased vegetation, which may disturb and weaken the correlation between the ISM and TP vegetation in late summer and early autumn.

**Author Contributions:** Conceptualization, H.-L.R.; methodology, H.-L.R. and G.L.; formal analysis, X.M.; data curation, X.M.; writing—original draft preparation, X.M., H.-L.R. and G.L.; writing—review and editing, X.M., H.-L.R. and G.L.; funding acquisition, B.S. and Y.S. All authors edited the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The National Key R&D Program of China (Grant 2022YFF0801603), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant 2019QZKK0105), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA2010030807), and the Basic Research Fund of CAMS (Grant 2021Z007) all provided financial support for this study.

**Data Availability Statement:** ECOCAST supplied the GIMMS NDVI3g data for this study, which can be obtained from the website at https://ecocast.arc.nasa.gov/data/pub/gimms/ (accessed on 29 November 2020). LAADS DAAC/NASA made the MCD19A3CMG data available on the website at https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 6 July 2023). The National Meteorological Information Center supplied the daily meteorological dataset of basic meteorological elements of China National Surface Weather Station (V3.0) on the website at http://data.cma.cn/ (accessed on 30 December 2021). The ERA5 reanalysis datasets were publicly available at https: //cds.climate.copernicus.eu/cdsapp#!/home/ (accessed on 21 October 2021). The NOAA PSL, Boulder, Colorado, USA, supplied the outgoing longwave radiation (OLR) data from their website at https://psl.noaa.gov (accessed on 21 October 2021). The JRA-55 dataset was publicly available at https://rda.ucar.edu/datasets/ds628.1/ (accessed on 5 October 2022). The Niño 3.4 index was publicly available at https://psl.noaa.gov/data/correlation/nina34.anom.data (accessed on 1 May 2023). The IOBM index was publicly available at http://cmdp.ncc-cma.net/Monitoring/cn\_nino\_index. php?product=cn\_nino\_index\_iobw (accessed on 1 May 2023).

**Acknowledgments:** We are grateful to LAADS DAAC/NASA and ECOCAST for supplying the remote sensing datasets. We thank National Meteorological Information Center for supplying all daily station-observed datasets. We thank NOAA PSL for providing the NOAA Interpolated Outgoing Longwave Radiation (OLR) dataset. We thank ECMWF and NCAR UCAR for providing data. We thank NOAA CPC for providing the Niño 3.4 index and NCC/CMA for providing the IOBM index.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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