Medrano S. C. auteur aut IRD Satgé Frédéric auteur aut IRD Molina-Carpio J. auteur aut IRD Zola R. P. auteur aut IRD Bonnet Marie-Paule auteur aut IRD text journalArticle eng text/pdf reformatted digital access This study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates (SPEs) from 10 km to 1 km. The method deliberates a set of variables that have close relationships with daily precipitation events in a Random Forest (RF) regression model. The considered variables include cloud optical thickness (COT), cloud effective radius (CER) an cloud water path (CWP), derived from MODIS, along with maximum and minimum temperature (Tx, Tn), derived from CHIRTS. Additionally, topographic features derived from ALOS-DEM are also investigated to improve the downscaling procedure. The approach consists of two main steps: firstly, the RF model training at the native 10 km spatial resolution of the studied SPEs (i.e., IMERG) using rain gauge observations as targets; secondly, the application of the trained RF model at a 1 km spatial resolution to downscale IMERG from 10 km to 1 km over a one-year period. To assess the reliability of the method, the RF model outcomes were compared with the rain gauge records not considered in the RF model training. Before the downscaling process, the CC, MAE and RMSE metrics were 0.32, 1.16 mm and 6.60 mm, respectively, and improved to 0.48, 0.99 mm and 4.68 mm after the downscaling process. This corresponds to improvements of 50%, 15% and 29%, respectively. Therefore, the method not only improves the spatial resolution of IMERG, but also its accuracy. specialized IMERG downscaling random forest model MODIS cloud optical and microphysical properties CHIRTS PEROU 021 126 020 14 9 1349 [17 ] 2023 https://www.documentation.ird.fr/hor/fdi:010090303 10.3390/atmos14091349 [F B010090303] https://www.documentation.ird.fr/hor/fdi:010090303 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2023-11/010090303.pdf IRD - Base Horizon / Pleins textes 2023-11-10 2025-02-24 fdi:010090303 fre