Resumen: Daily precipitation datasets are usually large, bulky and hard to handle, but they are of key importance in many environmental studies. We developed a tool to create custom datasets from observed daily precipitation records. Reference values (RV) are computed for each day and location using multivariate logistic regression with altitude, latitude and longitude as covariates. The operations were compiled in an Open Source R package called reddPrec. The reddPrec package consists of a set of functions used to: i) apply a comprehensive quality control over original daily precipitation datasets, flagging suspect data based on five predefined criteria; ii) fill missing values in original data series by estimating precipitation values using the 10 nearest observations for each day; and iii) create new series and gridded datasets in locations where no data were recorded. Idioma: Inglés DOI: 10.1016/j.envsoft.2016.11.005 Año: 2017 Publicado en: ENVIRONMENTAL MODELLING & SOFTWARE 89 (2017), 190-195 ISSN: 1364-8152 Factor impacto JCR: 4.177 (2017) Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 9 / 105 = 0.086 (2017) - Q1 - T1 Categ. JCR: ENVIRONMENTAL SCIENCES rank: 43 / 241 = 0.178 (2017) - Q1 - T1 Categ. JCR: ENGINEERING, ENVIRONMENTAL rank: 14 / 50 = 0.28 (2017) - Q2 - T1 Factor impacto SCIMAGO: 1.963 - Ecological Modeling (Q1) - Software (Q1) - Environmental Engineering (Q1)