A new algorithm is described for estimating surface rainfall rates based on lightning and satellite infrared observations. Parametrizations for convective and total rain area as well as rain rate relationships are obtained for clouds with and without lightning. The parameters are evaluated using as reference the three-dimensional precipitation fields and rain classification information from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A total of 631 TRMM PR orbits during the December 1997 to January 1998 calibration period, which consist of 366 lightning and 3103 non-lightning clouds, were used to determine the relationship parameter values. The algorithm error statistics are evaluated based on independent TRMM-PR measurements in February 1998 and rain gauges measurements from a network in Florida during the December 1997-February 1998 period. Overall, the algorithm underestimates the rain area for both cloud types by about 20%, while for the rain volume there is an overestimation of ~19% for lightning and ~12% for non-lightning clouds. Comparison of the mean diurnal cycle of rainfall against the one derived from the PR showed good agreement and low diurnal dependence of bias. Histograms of retrieved convective and stratiform rainfall are similar to the ones derived from PR. Comparison of hourly estimates with rain gauges revealed that the algorithm has an overall overestimation of about 6%. At monthly scales the biases are 2.4% and 0.27% for 1 and 4 degree resolution grids, respectively. The significance of lightning information on rainfall estimation accuracy is investigated by applying the proposed technique without lightning information. Comparisons with the TRMM-PR showed that in rain area determination there is an overall bias reduction of 11% when using lightning measurements. In rain volume the non-lightning scenario gives an underestimation of 24%. In rain gauge comparisons, the bias reduction from incorporating lightning data is more pronounced. It varies from 10% to 38% in case of monthly estimates, and up to 87% for the instantaneous 0.1-degree estimates. Correlation wise the increase in monthly estimates is 0.05 and 0.13 respectively for the PR and gauge validation data.