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

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 <i>r</i> = 0.79), followed by ERA5-Land (<i>r</i> = 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 (<i>r</i> ≈ 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 ( &gt;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.