Precipitation is one of the most important components of the hydrological cycle (Eltahir & Bras, 1996;Oki & Kanae, 2006) and great efforts have been invested in monitoring its spatiotemporal variability (Arabzadeh et al., 2020;Koohi et al., 2021;Teague & Gallicchio, 2017). Multiple methods have been developed to measure and monitor precipitation in the field, but accurate estimation of precipitation is still challenging (Adhikari et al., 2020;Foufoula-Georgiou et al., 2020). For example, this difficulty is due to, high spatiotemporal variability in complex topographical regions (Beck et al., 2019) and different sources of uncertainty associated with different measurement methods and scales (Behrangi & Wen, 2017). These uncertainties impede the study of climate and interconnections of hydroclimatic variables, water resources management, and hydrological forecasts (Foufoula-Georgiou et al., 2020;Sun et al., 2018).There are three main sources of precipitation data: (a) ground-based data (mainly from precipitation gauge networks), (b) satellite remote sensing (RS) data, and (c) reanalysis data sets (Hosseini-Moghari et al., 2018;Shayeghi et al., 2020;Sun et al., 2018). Each of these has its strengths and weaknesses. Gauges provide the most accurate measurements, but they are geographically sparse, especially in remote areas, inaccessible topographies, and harsh climates (
