Vulnerability to climate change refers to the degree to which a system, community, or population is susceptible to, or unable to cope with, the adverse impacts of climate change. Analysing vulnerability requires a systematic assessment of exposure, sensitivity, and adaptive capacity. Since climate change is multi-dimensional and affects ecological, social, and economic systems, methods of analysing vulnerability must integrate both quantitative and qualitative approaches. These methods help identify who or what is most at risk, where risks are concentrated, and how interventions can be prioritized. Several tools and frameworks have been developed over time to capture these complexities, drawing upon natural sciences, social sciences, and policy studies. One of the most widely used methods is the indicator-based approach. This method involves identifying key indicators of exposure, sensitivity, and adaptive capacity, assigning them values, and integrating them into an index that represents vulnerability. For instance, exposure indicators may include frequency of floods, droughts, or temperature extremes; sensitivity indicators could be crop dependency, population density, or health status; while adaptive capacity indicators may include literacy rates, income levels, or institutional support. The advantage of this approach lies in its ability to integrate diverse datasets into a composite picture that can be compared across regions or groups. However, indicator selection can be subjective, and the weighting of indicators may introduce bias. Despite these limitations, indicator-based vulnerability indices have been widely applied at global, national, and regional levels to inform climate policies. Another important method is participatory rural appraisal and community-based assessments. These methods prioritize local knowledge, perceptions, and experiences of climate impacts. They involve focus group discussions, mapping exercises, seasonal calendars, and livelihood analysis conducted with affected communities. Such approaches are valuable because they capture the lived realities of people, particularly marginalized populations who may not be represented adequately in top-down models. They also encourage community ownership of the results, which is essential for building resilience and adaptive strategies. The limitation, however, is that local assessments may lack comparability across regions and may be constrained by time, resources, or the inclusivity of participation. Scenario analysis and modeling techniques are also commonly employed. Climate models such as General Circulation Models (GCMs) or Regional Climate Models (RCMs) provide projections of temperature, precipitation, and extreme events under different greenhouse gas emission scenarios. These outputs can be coupled with sectoral models, such as crop yield models, water availability models, or health risk models, to estimate the future impacts of climate change on specific systems. Vulnerability is then assessed by linking these impact estimates with social and economic conditions. For example, a model may project reduced maize yields under rising temperatures, but vulnerability will depend on whether farmers have access to irrigation, financial credit, or alternative livelihoods. Modeling-based approaches allow for exploring future risks under uncertainty, but they require technical expertise, reliable data, and computational resources, which may not always be available