Access to data related to precipitation events is critically important in hydrometeorological assessments at the watershed level. On the other hand, access to ground data is often limited, thus making the application of satellite-derived precipitation data more significant. Due to indirect measurement and the nature of remote sensing, satellite products are systematically biased compared to ground station data, which must be evaluated at each location. Therefore, this study examined the performance of GPM satellite precipitation products and the correction of errors and biases in this data. In this research, daily precipitation data from the GPM satellite and data from 27 synoptic stations in the Karun River Basin were evaluated for the time period from 2014 to 2019. To correct the bias in GPM satellite precipitation, the Frank and Clayton copula functions were used, with the dependency parameters of these functions calculated using the PSO, HO, and TSA algorithms. For result evaluation, the statistics Bias, RBias, RMSE, CC, POD, CSI, and FAR were used. The results indicated that the GPM satellite estimates precipitation more accurately in lower altitudes compared to higher regions, and the Clayton copula function, with the dependency parameter computed using the exploratory PSO algorithm, showed improvements of 1.13% in Bias, 3.14% in RMSE, and 2.22% in CC, providing better results compared to other algorithms used for correcting errors and biases in GPM satellite precipitation products.