A major issue limiting the successful deployment of deep learning algorithms in geophysical applications is their inability to generalize to new contexts. Regarding the quantitative precipitation estimation (QPE) from the Global Precipitation Mission (GPM) satellite constellation, the GPM Microwave Imager (GMI) contains enough co-located brightness temperatures and rain rates data to train a deep learning inverse model to retrieve precipitation intensity. However, the difference in instrumental configurations makes it impossible to directly apply this inverse operator to another space-borne radiometric imager. A domain adaptation is thus necessary to solve the domain shift problem encountered when applying the model trained on one satellite to another satellite. The present paper tests a method to map the SSMI/S data to the GMI data. In the absence of sufficient paired images between the two satellites, we applied a Cycle consistent Generative Adversarial Network (CycleGAN), which allows for an Unsupervised Domain Adaptation approach. Evaluating the quality of adapted images is a complex problem. This paper employs two tactics: a brief evaluation of adapted radiometric images and a qualitative/quantitative evaluation of rain retrieval. Over several case studies, the results show that the domain adaptation step produces adapted SSMI/S images that retain the majority of the rain structure. Next, the rain detection score and intensity bias are then compared using 847 overpasses. The same analysis is carried out over mainland France by comparing the results with rainfall products supplied by Météo-France. In both comparisons, the adapted images allow the inverse operator to provide a better score in rain detection and intensity.