The primary weather forecast challenge at the Cape Canaveral Air Station and Kennedy Space Center is lightning. This paper describes a statistical approach that combines integrated precipitable water vapor (IPWV) data from a global positioning system (GPS) receiver site located at the Kennedy Space Center (KSC) with other meteorological data to develop a new GPS lightning index. The goal of this effort is to increase the forecasting skill and lead time for prediction of a first strike at the KSC. Statistical regression methods are used to identify predictors that contribute skill in forecasting a lightning event. Four predictors were identified out of a field of 23 predictors that were tested, determined using data from the 1999 summer thunderstorm season. They are maximum electric field mill values, GPS IPWV, 9-h change in IPWV, and K index. The GPS lightning index is a binary logistic regression model made up of coefficients multiplying the four predictors. When time series of the GPS lightning index are plotted, a common pattern emerges several hours prior to a lightning event. Whenever the GPS lightning index falls to 0.7 or below, lightning occurs within the next 12.5 h. An index threshold value of 0.7 was determined from the data for lightning prediction. Forecasting time constraints based on KSC weather notification requirements were incorporated into the verification. Forecast verification results obtained by using a contingency table revealed a 26.2% decrease from the KSC's previous-season false alarm rates for a nonindependent period and a 13.2% decrease in false alarm rates for an independent test season using the GPS lightning index. In addition, the index improved the KSC desired lead time by nearly 10%. Although the lightning strike window of 12 h is long, the GPS lightning index provides useful guidance to the forecaster in preparing lighting forecasts, when combined with other resources such as radar and satellite data. Future testing of the GPS lightning index and the prospect of using the logistic regression approach in forecasting related weather hazards are discussed.