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Active and passive microwave remote sensing estimates of surface precipitation based on signals from hydrometeors aloft require correction for evaporated precipitation that would otherwise reach the ground. This paper develops and compares two near-surface evaporation correction methods using two years of data from 509 globally distributed rain gauges and three passive millimeter-wave Advanced Microwave Sounding Units (AMSUs) aboard National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-15, NOAA-16, and NOAA-18). The first type of correction is a scale factor that minimizes the bias between the means of annual AMSU and rain gauge precipitation accumulations (in millimeters per year) for each of 12 infrared-based surface classifications. The scale factor for the second correction method is computed using a neural network trained using both surface classification and annual average relative humidity pro files. AMSU surface precipitation retrievals using both methods were compared to the annual accumulations for 509 rain gauges uncorrected for wind effects, where different data were used for training and accuracy evaluation. The rms annual accumulation retrieval errors for AMSU using surface classification and relative humidity corrections were 236 and 222 mm/y, respectively, compared to 190 mm/y for corresponding Global Precipitation Climatology Project data, which utilizes more satellite sensors and over 6500 rain gauges.