Global monthly rainfall estimates have been produced from over 8 years of measurements from the Defense Meteorological Satellite Program series of special sensor microwave/imagers (SSM/Is) and are analyzed to depict seasonal, annual, and interannual variability. This SSM/I product is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. The primary algorithm used is an 85 GHz scattering-based algorithm over land, and a combined 85 GHz scattering and 19/37 GHz emission over ocean, both of which have been calibrated with ground-based radar data. Errors associated with the SSM/I derived monthly rainfall are characterized through comparisons with various gauge-based, climatological, and other satellite-derived rainfall estimates. During the period of June 1990 to December 1991 the 85 GHz channels aboard the SSM/I failed, so no monthly rainfall estimates are available. An alternative algorithm, using a newly developed 37 GHz scattering approach over land, and emission only over ocean, was developed to obtain a continuous record of rainfall estimates for the entire SSM/I time series. Although the 37 GHz scattering algorithm is sensitive to rain rates in excess of 8 mm/h, the correlation between the 37 and 85 GHz monthly estimates over land can be as high as 0.9 (but varies regionally) when comparing both approaches during a period of useable 85 GHz measurements. The error in the monthly rainfall using this algorithm is typically larger in comparison with measurements from rain gauges. Over ocean the emission only algorithm produces a lesser amount of rain than the scattering-based algorithm, most likely attributed to the lack of a proper beam-filling correction. During the period of January 1992 to the present there were two SSM/I satellites in full operation, with sampling times of approximately 0600/1800 and 1000/2200 LT. Comparisons between the single and dual satellites are made and are compared with gauge data sets. In general, it is found that the dual-satellite estimates reduce the RMS errors, although the improvements are both regionally and seasonally dependent.