A general issue in climate science is to handle big data and run complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that Random Forest (RF) outperformed other ML methods. Explainable Artificial Intelligence (XAI) was further used to interpret the RF method and explain the impact of different LC changes on temperature. The results mainly agree with the climate science literature, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation and XAI.