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. Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towardsmore dynamic inter- and intra-annual assessments. In this regard, the mainchallenge is still represented by the limited availability of high temporalresolution rainfall data needed to estimate rainfall erosivity. As theavailability of high temporal resolution rainfall data will most likely notincrease in future decades since the monitoring networks have been decliningsince the 1980s, the suitability of alternative approaches to estimateglobal rainfall erosivity using satellite-based rainfall data was exploredin this study. For this purpose, we used the high spatial and temporalresolution global precipitation estimates obtained with the NationalOceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for theestimation of rainfall erosivity on a global scale. Alternatively, theerosivity density (ED) concept was also used to estimate global rainfallerosivity. The obtained global estimates of rainfall erosivity werevalidated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPHestimates have a marked tendency to underestimate rainfall erosivity whencompared to the GloREDa estimates. The most substantial underestimationswere observed in areas with the highest rainfall erosivity values. At thecontinental level, the best agreement between annual CMORPH and interpolatedGloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analysesconducted at the monthly scale for Europe revealed seasonal misalignments,with the occurrence of underestimation of the CMORPH estimates in the summerperiod and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity wasfound for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions,while in high-erosivity regions (&gt; 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (&gt; 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach toestimate rainfall erosivity yielded better agreement with GloREDa estimatescompared to CMORPH, which could be regarded as an expected result since thisapproach indirectly uses the GloREDa data. The application of asimple-linear function correction of the CMORPH data was applied to providea better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. Apreliminary trend analysis of the CMORPH rainfall erosivity estimates wasalso performed for the 1998–2019 period to investigate possible changes inthe rainfall erosivity at a global scale, which has not yet been conductedusing high-frequency data such as CMORPH. According to this trend analysis,an increasing and statistically significant trend was more frequentlyobserved than a decreasing trend.

Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity