The purpose of this paper is present the results of a developed, and implemented a Fuzzy rule based model to determine the probability of precipitation in the Marion Illinois area during the summer months. The model employs fuzzy logic and the results are compared to actual data to measure how reliable and viable this method is as an option in the precipitation prediction. Researchers, over the years, have been developing models for simulating, predicting and analyzing atmospheric phenomena, in order to accurately determine their immediate and long term effects on the environment and the quality of human life, such as in agriculture, ecosystem evolution, biodiversity, and disaster preparedness (decision support systems: drought/ warning or flash floods). To simulate global climate changes, researchers use General Circulation Models (GCMs). These have been developed using numerical weather predictions. These models are very useful for global impact studies such as global warming, but they are limited if applied to regional scenarios. This is because they are not able to neither simulate the local effects nor accurately present spatial and temporal resolutions. Continued work to improve the efficiency of these systems has led to the development of various models. Improvements have come in the form of Regional Climate Models; they have higher resolution and take into account orographic effects. These models use downscaling techniques, which bridge the gap between the global climate simulations and regional climate impact assessment. This paper presents the implementation of a fuzzy rule-based downscaling technique with specific application to the Marion, Illinois area. It shows that this method has the distinct advantage of being both computation and resource inexpensive while producing accurate information in a timely manner.