Numerous authors have demonstrated the theoretical and practical utility of lightning data for both identifying regions of heavy convective precipitation and estimating the rate of rainfall in these regions. In this work, lightning data have been incorporated into the real-time Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm. SCaMPR uses infrared and microwave data together to produce estimates of precipitation at very fine scales in time (every 15 min) and in space (at 4-km resolution). The algorithm is highly adaptable, as it uses discriminant analysis and stepwise forward linear regression to select from a pool of potential predictors and to calibrate them in real time to produce optimal performance in both discriminating raining from non-raining clouds and in estimating rainfall rates. The impact of lightning data on SCaMPR performance will be illustrated using both statistical analysis and description of the specific ways in which SCaMPR makes use of the data in its calibration.