Meteorological radar data are essential for meteorological monitoring, forecasting, and research, and it plays a crucial role in observing and warning of extreme weather risks. However, meteorological radars have some limitations, such as uneven distribution and severe topographical influence. Meteorological remote sensing satellites can partially overcome these limitations by providing larger observational scope and high spatial and temporal resolution. Using data from meteorological remote sensing satellites to train radar reflectivity factor retrieval models can effectively compensate for the missing and poor quality of radar data. However, there are still some challenges, such as extracting the features of intense convective weather with unclear coverage from complex multichannel meteorological remote sensing satellite data and removing the interference caused by nonprecipitation clouds on retrieval models. Moreover, the privacy and security of remote sensing data transmission need to be ensured. In this article, we propose a novel method that combines the advanced encryption standard method to protect the transmission of remote sensing data, a multiscale feature fusion module to extract multiscale features from multichannel meteorological remote sensing satellite data, and an attention technique to reduce the interference of nonprecipitation clouds on retrieval models. We conduct comparison experiments with multiple indicators to demonstrate that our method has certain advantages in retrieving radar reflectivity values of different sizes. Our method achieves 0.63, 0.36, 0.49, 0.55, and 0.99 on probability of detection, false alarm ratio, critical success index, Heidke skill score, and accuracy scores, respectively.