Visible and infrared (VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal resolutions. Combined measurements from visible/infrared scanner (VIRS) and precipitation radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness (COT), effective radius (Re), and brightness temperature of VIRS channel 4 (BT4), are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Parameters information (PISCP). It is essentially a lookup table (LUT) approach that is deduced from the optimal equitable threat score (ETS) statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the performance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic areas. Especially, over ocean, precipitating clouds (PCs) and non-precipitating clouds (N-PCs) are well distinguished by PISCP, with the probability of detection (POD) near 0.80, the probability of false detection (POFD) about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.