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Global warming's impact on high temperatures in various parts of the worldhas raised concerns. This study investigates long-term temperature trends infour major Ghanaian cities representing distinct climatic zones. Using NASA'sPrediction of Worldwide Energy Resource (POWER) data, statistical analysesassess local climate warming and its implications. Linear regression trendanalysis and eXtreme Gradient Boosting (XGBoost) machine learning predicttemperature variations. Land Surface Temperature (LST) profile maps generatedfrom the RSLab platform enhance accuracy. Results reveal local warming trends,particularly in industrialized Accra. Demographic factors aren't significant.XGBoost model's low Root Mean Square Error (RMSE) scores demonstrateeffectiveness in capturing temperature patterns. Wa unexpectedly has thehighest mean temperature. Estimated mean temperatures for mid-2023 are: Accra27.86{\deg}C, Kumasi 27.15{\deg}C, Kete-Krachi 29.39{\deg}C, and Wa30.76{\deg}C. These findings improve understanding of local climate warming forpolicymakers and communities, aiding climate change strategies.

Global Warming In Ghana's Major Cities Based on Statistical Analysis of NASA's POWER Over 3-Decades