The high mountain areas have seen a significant increase in air temperature and altered precipitation patterns over the last few decades. There is an urgent need for comprehensive analysis of climatic parameters at the regional level to address the parameter linkages. In this research work, the Multi-Scale Event Synchronization (MSES), the Hurst exponent (H), the fractal dimension (FD), entropy analysis, Monte Carlo simulation, and ordinary least squares (OLS) regression were used to examine long-term climatic parameters. The different reanalyzed data platform (GEE) provides precipitation (PPT), land surface temperature (LST), and normalized difference of vegetation index (NDVI) variability during the period 2001–2022 over the foothills of the Himalayan region (Doon Valley), which has been analyzed. The MSES were able to effectively capture multi-scale climatic patterns, whereas FD reveals persistent behavior in the NDVI-PPT, LST-NDVI, LST-PPT, and PPT-NDVI and anti-persistence in NDVI–LST and PPT–LST. The statistical significance of each variable was confirmed by Monte Carlo simulations, with values of 0.856 for NDVI, 0.653 for PPT, and 0.585 for LST. A spatial and temporal positive trend in LST and PPT reveals that there is an interlinkage in the climatic variables, which have significant seasonal differences and exhibit a significant climatic gradient during summer. Entropy and MSES analyses provide an understanding of the interactions between climatic variables at various time scales. The findings highlighted the need for long-term local climate monitoring in mountain ecosystems to assess local climate change impacts and to understand the linkages among the various climatic parameters that are vital for environmental management and ecosystem sustainability in the view of climate change scenarios.