Climate models are essential tools for understanding and predicting the Earth’s future climate, as they integrate physical, chemical, and biological processes governing the atmosphere, oceans, land surface, and cryosphere into a single framework. By using mathematical equations derived from the laws of physics, fluid dynamics, and thermodynamics, they simulate the complex interactions among various components of the climate system. These models allow scientists to examine how changes in natural and anthropogenic factors, such as solar radiation, greenhouse gas concentrations, aerosols, and land use, influence temperature, precipitation, sea level, and other climatic variables over time. They provide insights into potential trajectories of global warming and its regional manifestations, offering valuable evidence for policymakers to design mitigation and adaptation strategies. General circulation models (GCMs), which form the backbone of climate prediction, divide the planet into a three-dimensional grid of cells representing the atmosphere and oceans. Within each grid cell, equations calculate energy balance, moisture transport, and momentum exchange, while boundary conditions are informed by observations. As computational capacity has expanded, model resolution has improved, enabling finer representation of atmospheric processes, ocean currents, and cloud formation. This enhancement allows models to better reproduce historical climate patterns, giving confidence in their projections for future scenarios. Coupled atmosphere–ocean models link the two major components of the climate system, accounting for feedbacks like sea surface temperature changes affecting atmospheric circulation. Climate models are run under a range of emissions scenarios, such as those outlined by the Intergovernmental Panel on Climate Change (IPCC), to estimate how greenhouse gas concentrations and aerosols may evolve. These scenarios, known as Representative Concentration Pathways (RCPs) or Shared Socioeconomic Pathways (SSPs), depict different trajectories of socioeconomic development, energy use, and technological progress. By applying these scenarios, models project a spectrum of outcomes, from strong mitigation leading to limited warming to high-emission pathways resulting in severe temperature increases. These projections inform international climate negotiations, national adaptation plans, and local resilience initiatives. Regional climate models (RCMs) downscale information from global models to finer spatial scales, capturing details such as topography, land–sea contrasts, and small-scale circulation patterns that global models cannot fully resolve. This is crucial for assessing climate impacts on water resources, agriculture, biodiversity, and urban infrastructure. For example, RCMs can simulate how monsoon systems or tropical cyclones might behave in a warming world, enabling governments to prepare for shifts in rainfall intensity or storm frequency. They also aid in estimating the probability of extreme events like heatwaves, droughts, and floods, which are critical for disaster risk management. Paleoclimate data, derived