Research Article | Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021)

A new study published in Big Earth Data provides a comprehensive evaluation of the accuracy of widely used satellite-based and reanalysis precipitation datasets, offering critical guidance for hydrological, climate, and environmental applications in Central Europe. 

Citation
Paluba, D., Bližňák, V., Müller, M., & Štych, P. (2025). Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021). Big Earth Data, 1–30. https://doi.org/10.1080/20964471.2025.2592444

Abstract

This study assesses the accuracy of ten satellite-based and reanalysis precipitation datasets available in Google Earth Engine (GEE) using in-situ rain gauge measurements across Czechia, Central Europe, from 2001 to 2021. The gauge-adjusted GSMaP dataset (GSMaP_GA) was the most accurate dataset overall (Pearson’s correlation coefficient r = 0.79), followed by ERA5-Land (r = 0.75), with both showing superior performance for rainy days above 1 mm of precipitation. In contrast, CHIRPS, GLDAS, and PERSIANN-CDR showed the weakest performance (r ≈ 0.41–0.42). All datasets overestimated precipitation on days with no or with very light rain (≤1 mm/day) and underestimated it during heavy rainfall events ( >5 mm/day). ERA5-Land systematically overestimated annual precipitation by 15–35%, while GSMaP_GA showed slight underestimation by 0.5–9%. Although absolute errors generally increased with elevation, GSMaP_GA showed the smallest elevation-related biases, highlighting the importance for gauge-adjustment. Part of the observed spatial and seasonal biases may be explained by the combination of coarse spatial resolution and the challenges of capturing short-lived summer convective storms over complex terrain. Overall, GSMaP_GA is recommended for most applications due to its superior accuracy, while ERA5-Land is suitable for long-term studies because of its long historical record extending back to the 1950s.

Keywords

Precipitation, reanalysis, Google Earth Engine, time series, Czechia

Big Earth Data is an interdisciplinary Open Access journal which aims to provide an efficient and high-quality platform for promoting the sharing, processing and analyses of Earth-related big data, thereby revolutionizing the cognition of the Earth’s systems. The journal publishes a wide range of content, including Research Articles, Review Articles, Data Notes, Technical Notes, and Perspectives. It is now included in ESCI (IF=3.8, Q1), Scopus (CiteScore=9.0, Q1), Ei Compendex, GEOBASE, and Inspec. Starting from 2023, Big Earth Data has announced a new award series for authors: Best and Outstanding Paper Awards.