Climate change is increasing the challenges related to extreme weather events, shifting precipitation patterns, causing water scarcity and increasing the occurrence of natural disasters. Accurate and timely precipitation data are critical for understanding and mitigating these events, as well as for informing decision-makers. Specifically, Europe climatic and physiographic features make capturing fine-scale (1 km-daily) variations crucial to improve the precision of climate models and facilitate targeted adaptation strategies in this area.

This can be achieved by using the recent remote sensing technologies, which allow to systematically monitor wide areas without the need of maintaining ground networks. In particular, for satellite precipitation estimation, both the top-down and bottom-up approaches have been exploited in recent years to obtain information related to rainfall. Both the methodologies carry advantages and limitations. Their merging, coupled with high spatial resolution ancillary information, is therefore recommended to reach the final aim of detailed and accurate precipitation data.

In this study, the rainfall data obtained from IMERG Late Run and SM2RAIN ASCAT (H SAF) are downscaled and merged over the whole Europe. The downscaling is obtained by leveraging high spatial resolution statistical information from CHELSA product, while a triple collocation technique is applied to merge the two downscaled datasets. The resulting high resolution rainfall is subsequently compared against multiple products, including coarse resolution ones such as H SAF, IMERG-LR, ERA5, EOBS, PERSIANN, CHIRP, GSMAP, and high-resolution products like EMO, INCA, SAIH, COMEPHORE, MCM, 4DMED. These comparisons, spanning ground, model and satellite data, serve to assess its capabilities in estimating precipitation over Europe.