The study presents a novel empirical approach to assess the sensitivity of global terrestrial ecosystems to climate variability. The authors develop the Vegetation Sensitivity Index (VSI), which identifies areas sensitive to climate variability over the past 14 years using MODIS-derived enhanced vegetation index (EVI) and three climatic variables: air temperature, water availability, and cloud cover. The analysis uses an autoregressive modeling approach to identify climate drivers of vegetation productivity on monthly timescales and regions with memory effects and reduced response rates to external forcing. Key findings include: 1. **Highly Sensitive Regions**: The Arctic tundra, parts of the boreal forest belt, tropical rainforests, alpine regions, steppe and prairie regions, the Caatinga deciduous forest, and eastern Australia exhibit high VSI values, indicating amplified responses to climate variability. 2. **Contribution of Climate Variables**: Different regions show varying sensitivities to different climate variables. For example, the Caatinga biome is most sensitive to variations in water availability, while alpine regions are strongly sensitive to temperature. 3. **Memory Effects**: Areas with low VSI values show strong memory effects, including drylands like the Sahel, Australian outback, and the Middle East, where vegetation productivity is influenced by past conditions rather than current climate. The study provides a quantitative methodology to assess the relative response rate of ecosystems to environmental variability, which is crucial for understanding ecosystem resilience and the impact on human well-being. The findings highlight the importance of identifying and prioritizing ecologically sensitive regions to address the challenges posed by climate change.The study presents a novel empirical approach to assess the sensitivity of global terrestrial ecosystems to climate variability. The authors develop the Vegetation Sensitivity Index (VSI), which identifies areas sensitive to climate variability over the past 14 years using MODIS-derived enhanced vegetation index (EVI) and three climatic variables: air temperature, water availability, and cloud cover. The analysis uses an autoregressive modeling approach to identify climate drivers of vegetation productivity on monthly timescales and regions with memory effects and reduced response rates to external forcing. Key findings include: 1. **Highly Sensitive Regions**: The Arctic tundra, parts of the boreal forest belt, tropical rainforests, alpine regions, steppe and prairie regions, the Caatinga deciduous forest, and eastern Australia exhibit high VSI values, indicating amplified responses to climate variability. 2. **Contribution of Climate Variables**: Different regions show varying sensitivities to different climate variables. For example, the Caatinga biome is most sensitive to variations in water availability, while alpine regions are strongly sensitive to temperature. 3. **Memory Effects**: Areas with low VSI values show strong memory effects, including drylands like the Sahel, Australian outback, and the Middle East, where vegetation productivity is influenced by past conditions rather than current climate. The study provides a quantitative methodology to assess the relative response rate of ecosystems to environmental variability, which is crucial for understanding ecosystem resilience and the impact on human well-being. The findings highlight the importance of identifying and prioritizing ecologically sensitive regions to address the challenges posed by climate change.