Enhancing daily precipitation reconstruction: An improved version of the reddPrec R package

Adrian Huerta , Stefan Brönnimann, Martín de Luis, Santiago Beguería, Roberto Serrano-Notivoli

Reconstructing high-quality daily precipitation series is essential for climate studies, hydrological modeling, and environmental applications. This work presents a new version of reddPrec, a versatile and flexible R package designed to reconstruct precipitation datasets through standard quality control, gap-filling, and grid creation procedures. The update introduces greater flexibility in spatial modeling, inclusion of dynamic covariates, and new modules for enhanced quality control and homogenization. Daily precipitation can now be predicted through machine learning approaches within a user-friendly framework, allowing users to select modeling approaches and customize settings. We demonstrate its capabilities through case studies in Switzerland and Spain, evaluating improvements in reconstruction accuracy, quality control, and homogenization. Enhanced quality control and homogenization procedures were specifically validated to ensure reliable adjustment and consistency of precipitation series. Overall, reddPrec provides a comprehensive and reliable tool for reconstructing precipitation series, supporting the creation of high-quality datasets for climate research and related fields.