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

This talk describes a high-resolution dataset of daily minimum (TN), mean (TG), and maximum (TX) near-surface temperatures in Poland from 1951 to 2020 with a 1 km2 spatial resolution developed using radial basis functions applied to quality-controlled observations from 347 ground weather stations at the Institute of Meteorology and Water Management – National Research Institute. The dataset was evaluated on a seasonal, monthly, and station basis by leave-one-out cross-validation (LOO-CV) to ensure the ability to reproduce the original variability in the characteristics. The findings indicate TG performs best in winter and spring, with high correlation and low root-mean-square deviation. Although TX's performance is slightly below average, it still shows relatively good agreement, especially in autumn. The method is less successful at accurately capturing TN, with the best results seen in winter. The analysis also confirms that the standard deviation of temperatures, a measure of variability, is consistent between observed and interpolated data. All variables exhibit biases of no more than 0.03 and minimal interannual variability. Pearson's correlation coefficient is very high, ranging from 0.94 to 0.98. The difference between the 5th and 95th percentiles suggests a slight underestimation of Q95 and an overestimation of Q05. There is a noticeable increase in seasonal RMSD variability with altitude, with relatively small interannual differences at lower altitudes and the greatest at the highest altitude class. Mean values are preserved, and the interpolated data's standard deviation is slightly lower. Finally, an example of the application of the resulting gridded product in the field of climate change is shown.
The validation results demonstrate the dataset's accuracy and suitability for climatological applications. However, some limitations and potential areas for improvement have been identified. Notably, while computationally efficient, the RBF interpolation method may smooth out extremes and underestimate spatial variability, particularly in areas with complex terrain. 
This open-access dataset is crucial for climate change impact studies at smaller scales and can serve a wide range of users, including researchers, administrative bodies, and society. One important application of such a dataset is as reference data for bias correction of regional dynamical downscaling results (e.g., the EURO-CORDEX initiative) to develop effective adaptation and mitigation strategies.

Jaczewski, A., Marosz, M., and Miętus, M.: PL1GD-T – gridded data of the mean, minimum and maximum daily air temperature (2 m) for the Polish area at a resolution of 1 km×1 km and the period 1951–2020, Data repository of IMGW-PIB, https://doi.org/10.26491/imgw_repo/PL1GD-T, 2024.
Jaczewski, A., Marosz, M., and Miętus, M.: PL1GD-T: a high-resolution gridded daily air temperature dataset for Poland, Earth Syst. Sci. Data, 17, 3857–3871, https://doi.org/10.5194/essd-17-3857-2025, 2025.