Evaporation is a vital process in the hydrological cycle, accounting for approximately 70% of water loss from the Earth’s surface. In semi-arid and rapidly urbanizing regions, such as Mbeya, Tanzania, understanding the meteorological drivers of evaporation is critical for water resource management and agricultural planning. This study utilized principal component analysis (PCA) on a 10-year dataset comprising solar radiation, sunshine hours, minimum and maximum temperatures, and wind speed to identify key factors influencing evaporation. Descriptive statistics revealed significant non-normality in most variables, particularly radiation and wind speed. At the same time, correlation analysis showed a strong positive relationship between sunshine hours and radiation (r= 0.66) and a moderate negative correlation between radiation and minimum temperature (r= −0.30). PCA identified two principal components accounting for 66.61% of the total variance. Component 1 (38.06%) captured solar-driven variability, dominated by sunshine duration and radiation, whereas Component 2 (28.55%) reflected thermal influences, particularly maximum and minimum temperatures. Wind speed contributed minimally, suggesting a more localized or less consistent role in evaporation dynamics. These findings demonstrate the value of PCA in simplifying complex climatic datasets and improving the interpretation of evaporation processes. Solar radiation and sunshine hours emerged as the dominant drivers, with temperature as a secondary influence. The results emphasize the need to integrate surface-level variables, such as land use, vegetation cover, and soil moisture, in future studies to capture spatial heterogeneity and improve predictive accuracy, especially in data-scarce, climate-sensitive regions like Mbeya.