A machine learning-based backward extension of IMERG daily precipitation over the Greater Alpine Region

Abstract

Accurate knowledge of precipitation at high spatio-temporal resolution is essential for climate studies and hydrological applications, particularly in mountainous regions where traditional models often underperform due to coarse resolution and sparse observational networks. In this study, we present a machine learning-based approach to enhance ERA5 reanalysis precipitation estimates using the satellite-derived IMERG (Integrated Multi-satellite Retrievals for GPM) product as a reference. We focus on the Greater Alpine Region (GAR), using extreme gradient boosting combined with Shapley additive explanations to identify the most influential ERA5 variables. This method enables the creation of a new daily rainfall dataset, ML-IMEX-GAR (Machine Learning IMERG backward-EXtended precipitation dataset over GAR), at IMERG’s spatial resolution for the historical period 1960–2000.

Compared to ERA5, ML-IMEX-GAR reduces the spatiotemporal RMSD against IMERG by approximately 14%, and achieves strong agreement with in-situ observational monthly data, with an $R^2$ of 0.87. These findings demonstrate the potential of machine learning to correct reanalysis biases, improve historical precipitation reconstructions, and support climate change research in data-scarce, complex terrains.