PL1GD-T: a high-resolution gridded daily air temperature dataset for Poland

Abstract. This paper presents a high-resolution gridded dataset of daily minimum (TN), mean (TG), and maximum (TX) near-surface air temperatures over Poland, covering the period from 1951 to 2020, with a spatial resolution of 1 km2. The PL1GD-T dataset was developed using radial basis functions (RBFs), which were applied to quality-controlled observations from 347 ground weather stations at the Institute of Meteorology and Water Management – National Research Institute. TG is calculated consistently using the same formula throughout the entire period, and daily TN, TG, and TX fields are generated based on available daily records. Cross-validation methods evaluated the gridding procedure on a monthly basis. The linear RBF was selected by hold-out cross-validation (HO-CV) as the most suitable for the gridding procedure among other RBFs. The leave-one-out cross-validation (LOO-CV) was performed to ensure the ability to reproduce the original characteristics variability. The values of the scores averaged over all stations for individual months are in the range of 0.3–0.2, 0.3–0.2, and 0.1–0.2 (K) for the bias; in the range of 1.23–1.46, 0.69–0.92, and 0.84–0.99 (K) for the root-mean-squared difference (RMSD); and in range of 0.91–0.97, 0.98–0.99, and 0.98–0.99 for the correlation for TN, TG, and TX, respectively. The RMSD is clearly altitude-dependent, increasing from lowland to mountainous regions. The dataset's scope and resolution allowed for the robust estimation of local climate variability characteristics and observed trends. The availability of high-resolution datasets in both spatial and temporal contexts is essential for climate change impact analysis on a smaller scale. This new dataset provides a quality-validated, high-resolution, and open-access dataset that could be utilised by society, administrative bodies, or research institutions for climate-related applications. The dataset is publicly available from the repository of the Institute of Meteorology and Water Management – National Research Institute at https://doi.org/10.26491/imgw_repo/PL1GD-T (Jaczewski et al., 2024).