## 1 Introduction  
Precipitation is an important part of the water cycle, and it drives the supply of water resources for the agriculture, industry, and even society as a whole [1]. Intense rainfall can trigger floods causing life-threatening situations [2; 3], and a lack of precipitation induces droughts [4], impacting the agricultural sector. The successive reports published by the Intergovernmental Panel on Climate Change [5] show that a warmer global climate will increase the number and intensity of extreme episodes, such as droughts and intense precipitations. [PERSON] et al. [6] have already observed a slight increase in precipitation over land at a global scale since the beginning of the 20 th century and of extreme precipitation events at the local scale. Over Europe, the number of extreme flood events has increased by 0.3% per year over the period from 1870 to 2016 [7]. Moreover, the observed precipitation fields at high spatial and temporal resolution can be used as inputs for meteorological, climatic, and hydrological numerical studies [8]. The monitoring of precipitation can be used to assess landslide and slope failure risks over mountainous areas [9; 10]. Precipitation estimates at near-real-time can be provided by three categories of instruments: rain-gauge networks, ground-based weather radars, and remote sensing facilities onboard satellites. Rain gauges permit the quantification of surface-rain amounts locally [11] and, structured in networks, they can provide sparse information on the spatial and temporal distributions of precipitation [1]. However, rain gauges cannot be installedover seas and oceans. The data coverage can be spaced out in sparsely populated areas, which is a limitation for global- or large-scale studies [12]. Weather radar provides useful information about the rain rate at a high spatial and temporal resolution. Nevertheless, as for the rain gauges, they cannot be installed over marine surfaces. Projects based on radar networks allow for the building of rain-products over larger areas, such as over France (PANTHERE project; [13]) or Europe (OPERA project; [14]). Note that all the acronyms used in this paper are defined in the Abbreviations. Largely developed in recent years, remote sensing observations from satellites have enabled improvements in precipitation estimates on a large spatial scale. Different sensors are used to estimate precipitation amounts: infrared (IR) or visible (VIS), and passive or active microwave (PMW or AMW) sensors. The IR and VIS observations are often used aboard geostationary (GEO) satellites [1], while AMW and PMW sensors are deployed in a constellation of Low Earth Orbiting (LEO) satellites which only cover a swath of the Earth's surface [15]. Commonly, the GEO-IR sensors measure the cloud-top brightness temperature (\(T_{b}\)) and, thanks to empirical relationships, can estimate rain rates at the surface. LEO-PMW radiometers measure the effect of large liquid and ice particles on the upwelling radiation coming from the Earth's surface to estimate rain rate [1], whereas spaceborne precipitation radars, called LEO-AMW sensors, use the information retrieved by the backscatter signal from rain-sized particles. Moreover, GEO-IR sensors permit the study of a unique part of the world with a high spatiotemporal resolution and can estimate the rain rates derived from cloud-top brightness temperature data by using empirical laws, while LEO-PMW and -AMW sensors, which have better sensing through clouds, have a lower spatiotemporal resolution [16]. In this framework, various IR and PMW/AMW sensor-based products have been developed to estimate surface precipitation during the last decades.