Precipitation is one of the main forcings for sea surface salinity (SSS). The accuracy of precipitation products over open oceans, however, remains a problem due to the lack of in situ measurements. In this study, we use an ocean model to test the performance of precipitation products in the Indian Ocean, where large salinity contrasts exist. The model consists of four active layers overlying a deep, inert ocean, the top-most layer being a mixed layer governed by Kraus-Turner physics. Solutions are forced by monthly-mean precipitation climatologies, and their surface salinity fields (S1) are compared with observed SSS. In the Indian Ocean, both river runoff and the Indonesian throughflow significantly influence the long-term mean SSS distribution, and hence adequate treatment of these boundary forcings is a prerequisite for the model to be useful as a tool for evaluating precipitation products. We also explored the sensitivity of solutions to various model parameters and forcing, making sure they are not the main source of the bias. The model produces the best annual-mean S1 field when it is forced by the Climate Prediction Center Merged Analysis of Precipitation (CMAP) product, a merged precipitation product based on rain-gauge data and several satellite estimates. An amplitude adjustment of the Global Precipitation Climatology Project (GPCP) product is suggested by the model S1, supporting the notion that merged products give reliable spatial patterns but not necessarily correct magnitudes. Problems with some other precipitation products are also discussed, in terms of their potential distortion of mixed-layer thickness.