Nowadays a well-established tool for Earth remote sensing is represented by Spaceborne synthetic aperture radars (SARs) operating at L-band and above that offers a microwave perspective at very high spatial resolution in almost all-weather conditions. Nevertheless, atmospheric precipitating clouds can significantly affect the signal backscattered from the ground surface on both amplitude and phase, as assessed by numerous recent works analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions. On the other hand, such sensitivity could allow detecting and quantifying precipitations through SARs. In this work, we propose an innovative processing framework aiming at producing X-SARs precipitation maps and cloud masks. While clouds masks allow the user to detect areas interested by precipitations, precipitation maps offer the unique opportunity to ingest within flood forecasting model precipitation data at the catchment scale. Indeed, several issues still need to be fully addressed. The proposed approach allows distinguishing flooded areas, precipitating clouds together with permanent water bodies. The detection procedure uses image segmentation techniques, fuzzy logic and ancillary data such as local incident angle map and land cover; an improved regression empirical algorithm gives the precipitation estimation. We have applied the proposed methodology to 16 study cases, acquired within TSX and CSK missions over Italy and United States. This choice allows analysing different typologies of events, and verifying the proposed methodology through the available local weather radar networks. In this work, we will discuss the results obtained until now in terms of improved rain cell localization and precipitation quantification.