## 1 Introduction  
Precipitation is an important chain of the water cycle and its spatiotemporal distribution is closely related to nature disasters such as floods and drought [1]. The latest research suggests climate changes have significantly affected the variability and trends of precipitation in China [2]. In 2020, the city of Shanghai suffered its longest plum rain season in the recent two decades. [PERSON] et al. also indicated that the probability of compound flooding from extreme precipitation and storm surge will become higher because of climate change [3]. Therefore, high-resolution and accurate precipitation measurement is crucial to study and monitor this changing risk, which also has a great significance on a numerical weather prediction (NWP) model that heavily relies on its initial conditions [4]. Existing methods for precipitation observation mainly include the tipping-bucket rain gauges (TBRGs) network and weather radars (WRs) [5]. A rain gauge network can provide more accurate rain intensity at fixed locations in the monitoring region, but its spatial representativeness is not sufficient for the disaster warning especially in complex terrain [6; 7]. The weather radar achieves regional quantitative precipitation estimations (QPEs) with higher spatiotemporal resolution [8]. However, the uncertainty of the relationship between radar reflectivity and rain intensity leads to the errors [9]. In mountain areas, the effects of beam blockage, ground clutter and bright band interception cannot be neglected [10; 11]. Moreover, the cost of operation, maintenance, and management for above two monitoring systems is quite expensive. In the applications of Internet of Things (IoT), the horizontal microwave links (HMLs) of commercial cellular communication networks has been suggested as an alternative approaches for near-ground precipitation monitoring [12; 13]. Compared with rain gauges and weather radars, the reconstruction of rainfall field by HMLs network basically depends on the existing infrastructure, which has a relatively low cost [14]. The HMLs can be considered as passive rainfall detection sensors, and the power-law relation between rain intensity and rain attenuation is used to quantitatively estimate precipitation. [PERSON] and [PERSON] have demonstrated that the resolution of HMLs measurement is higher than that of the gauge network [15]. Many experiments have proven that this method is very useful to measure precipitation in support of existing observation systems [14; 16; 17]. Moreover, some studies have indicated that the links of commercial 5G networks can also be used to reconstruct a rainfall field [18]. In other applications, the HMLs have been employed to the classification of rainfall type, radar calibration and raindrop size distribution retrieval [19; 20; 21]. Inspired by the utilization of HMLs, the oblique earth-space links (OELs) between ground and satellite have been studied for rainfall monitoring [22; 23; 24; 25]. As another source of microwave links, the deployment of the OELs rainfall observation system cannot only use the existing equipment but also has a lower installation cost [26]. One sensor typically comprises a low noise block downconverter (LNB) and a common dish antenna. Most of the published studies focus on the rain intensity inversion by geostationary earth orbit (GEO) satellites OELs. [PERSON] et al. made use of the Ku band OELs to detect rainfall in Paris, and the measurements were compared with rain gauge and weather radar. Their results demonstrated that the OELs are very useful in hilly areas where the radar data cannot be obtained [22]. Differently to the HMLs, there are more complex atmospheric factors, such as gas, cloud, turbulence and melting layer, affecting the rainfall inversion by OELs [27]. In our previous work, we analyzed the characteristics of signal variation and proposed a novel approach to address two critical issues in OEL rainfall inversion: the identification of rain-no-rain period and the determination of signal reference level [28].