The article “RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements” was accepted for publication in December 2019 and made available online on January 2nd 2020 in the Remote Sensing of Environment journal.

This work presents the Random Forest based MErging Procedure (RF-MEP), which allows to combine information from ground-based measurements, satellite-based precipitation products, and topography-related features to improve the representation of the spatio-temporal distribution of precipitation, especially in data-scarce regions. RF-MEP is applied over Chile for 2000-2016, using daily measurements from 258 rain gauges for model training and 111 stations for validation. Two merged datasets were computed: RF-MEP3P (based on PERSIANN-CDR, ERA-Interim, and CHIRPSv2) and RF-MEP5P (which additionally includes CMORPHv1 and TRMM 3B42v7). Our results suggest that RF-MEP could successfully be applied to other regions and to correct other climatological variables. The RFmerge R package, which implements RF-MEP, is freely available online at https://github.com/hzambran/RFmerge.