<![CDATA[Changes in temperature patterns due to climate change are a global challenge that requires in-depth analysis, especially in tropical regions such as the city of Medan, Indonesia. This research aims to project future temperature patterns using the Monte Carlo simulation method, utilizing historical data on daily average temperatures from the Meteorology, Climatology and Geophysics Agency (BMKG). A probability-based Monte Carlo method is used to analyze the future temperature distribution, applying the normal distribution as the basic model. Parameters such as mean and standard deviation are calculated accurately, and thousands of iterations are performed to ensure stable and representative simulation results. The analysis process is carried out using Python and supporting libraries such as NumPy, SciPy, and Matplotlib, which provide flexibility and efficiency in environmental data processing. The results of this study show that the Monte Carlo method can produce future temperature distributions that reflect daily temperature variations as well as the probability of extreme events. These predictions provide important insights for various sectors, including health, energy and urban planning, in developing strategic plans to deal with the impacts of climate change. This research confirms that Monte Carlo simulation is an effective approach for analyzing climate data in tropical regions. Additionally, this research opens up opportunities for further development, such as integrating additional data and adapting the model to different environmental scenarios to improve prediction accuracy and relevance of results.]]>
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