We examined statistical relationships between the seasonal Southern Oscillation Index (SOI) and annual total acreages burned and the number of fire in Hawaii. The results show that summer is the most favorable season for fire activities. A composite of total acres burned during four ENSO events reveals that a large total acres burned is likely to occur from spring to summer in the year following an ENSO event. The correlation is most significant between the total acres burned in summer and the SOI of the antecedent winter. This relationship provides a potential for long-lead (i.e. 2 seasons in advance) prediction of wildfire activity in Hawaii. Simple regression and logistic regression models are developed to test fire predictability. Large year-to-year variations of fire data impair the efficiency of the simple regression prediction. To overcome this problem, logistic regression is applied to predict events of large acreages burned by wildfires. The goodness of predictions is measured by specificity, sensitivity, and correctness using a cross validation method. A comparison between the simple regression and logistic regression shows that a logistic regression model is better in seasonal fire prediction than the simple regression model.