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.
K eywords
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.
Big Earth Data
Data/statistical analysis
Not applicable
Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021)
2-Dec-2025