Due to Global Warming, Earth's climate zones shift outwards from the equator towards the poles. In polar warm several times faster than anywhere else, a phenomenon called Polar Amplification. The Intergovernmental Panel on Climate Change lays out several possible scenarios for the future of Earth's climate, called Shared Socioeconomic Pathways. Simulations, such as the Coupled Model Intercomparison Project (CMIP), model these scenarios, providing worldwide maps of climatic conditions for every year until 2100. The availability of satellite-based Earth observation data and global climate forecasts allows for the detection of climatic changes between arbitrary points in time. Considering a north-south strip of land, changes to prevailing climatic conditions can be expected to first occur closer to the equator, and later further polewards. Inverting this approach, when studying the future of a specific study site, it may be possible to identify off-site areas closer to the equator which have historically undergone climatic changes similar to future changes expected to occur in the original study area. By studying environmental changes at the off-site location, researchers could then draw conclusions about what may happen at their original study area in the future. We demonstrate this concept using the Yamal peninsula in northwestern Siberia as an example, which extends across 700km north to south and features relatively plain terrain, allowing for the study of the gradual polewards creep of climate zones without significant influences from terrain elevation. Asking how well predicted climatic changes for the future (2022-2037) of the Yamal peninsula correspond to historical (2007-2022) changes further south in the study area, and how well such theoretical areas of correspondence can be used to extract historical trends in snow characteristics and biomass, we analyse CMIP Phase 6, NASA Famine Early Warning Systems Network Land Data Assimilation System, and ESA Biomass Climate Change Initiative data using a k-means classification and change detection approach. We successfully identify areas of correspondence and extrapolate historical changes to the local environment. Follow-up studies could transfer this approach to other environmental properties, use alternative or additional data sources, vary time frames, or use this approach for training data selection for machine-learning models. Lastly, the applicability of this approach warrants on-site verification.