Abstract High-resolution climate data, both spatially and temporally, are essential for enhancing our understanding of climate change and mitigating its environmental and societal impacts. However, challenges such as the sparse distribution of meteorological stations and the limited duration of statistical data collection have led to climate data being predominantly available at lower temporal and spatial resolutions, in many countries worldwide, including Tunisia. This limitation hampers accurate climate impact assessments and the implementation of effective adaptation measures. Hence, this study aimed to generate high-resolution (~ 1 km) monthly precipitation and air temperature data for Tunisia (1950–2023) by downscaling the ERA5-Land reanalysis dataset (0.1° × 0.1°, monthly) using the delta downscaling framework and the KrigR R package. After a comprehensive evaluation against observations from 26 precipitation stations and 28 temperature stations from the GSOD dataset, the KrigR R package method demonstrated the highest accuracy and was selected as the final product. This highlights KrigR as a robust and effective approach to traditional delta downscaling for producing high-resolution climate data. The downscaled data allows for precise analysis of temperature and precipitation trends across Tunisia. The downscaled data reveals a clear warming trend in Tunisia, with an average temperature increase of 0.27 °C per decade and a general decline in precipitation, averaging 3.63 mm per decade. However, statistically significant precipitation trends are limited to certain regions. The spatial detail provided by the downscaled data offers valuable insights for further regional climate change studies. This high-resolution methodology can also inform future assessments of climate impacts and adaptation strategies in areas lacking such detailed data.
Taheri et al. (Thu,) studied this question.