Tar spot of corn, caused by the obligate fungal pathogen Phyllachora maydis, was first detected in the United States in 2015 in Illinois and Indiana. Since then, tar spot has been detected in Florida, Iowa, Michigan, Ohio, and Wisconsin. The epidemiology of the tar spot fungus has been studied in Mexico. A limited amount of information is available on the weather conditions that favor the development of tar spot and if these conditions can predict future epidemics in the Midwest. Tar spot is favored by cool conditions (monthly average of 15-21°C) and high relative humidity (monthly averages above 75%). Based on this information and data from epidemics in Wisconsin and Michigan in 2018, a preliminary disease prediction model was developed for testing in 2019. The underlying framework of the prediction model uses logistic regression with monthly mean air temperature and relative humidity inputs. The output results in a prediction of risk of tar spot development on a daily basis and suggests if a fungicide application is necessary to reduce disease. The model has been programmed into a smartphone application called Tarspotter, which is being beta-tested in 2019. Accurately predicting local tar spot epidemics in corn can help farmers make accurate, and environmentally and economically responsible fungicide applications.