LAUSR

In this study, a methodology that can reconstruct missing daily values of maximum and minimum temperatures over a long time period under the assumption of a sparse network of meteorological stations is described. To achieve this, a well-established software used for quality control, homogenization and the infilling of missing climatological series data, Climatol, is used to combine a mosaic of data, including daily observations from 15 European stations and daily data from two high-resolution reanalysis datasets, ERA5-Land and MESCAN-SURFEX; this is in order reconstruct daily values over the 2000–2018 period. By comparing frequently used indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in studies of climate change assessment and goodness-of-fit measures, the reconstructed time series are evaluated against the observed ones. The analysis reveals that the ERA5-Land reconstructions outperform the MESCAN-SURFEX ones when compared to the observations in terms of biases, the various indices evaluated, and in terms of the goodness of fit for both the daily maximum and minimum temperatures. In addition, the magnitude and significance of the observed long-term temporal trends maintained in the reconstructions, in the majority of the stations examined, for both the daily maximum and daily minimum temperatures, is an issue of the greatest relevance in many climatic studies.