Given the growing concerns about climate change, climatic data is particularly important for the scientific community and society in general, as decisions of individuals, business and governments are dependent on available meteorological data1. At present, a large number of the large-scale precipitation datasets are publicly available, although formats and completeness of the records vary widely2. Heavy rainfall and extreme events are of major importance for climate change, economy and society3. However, extreme events are typically rare events of short duration and in many regions of the world only limited observational data of sufficient temporal resolution is available to capture them4. High temporal resolution rainfall measurements are important in this context, but also have been recognised to be very useful for urban drainage models5, climate change modelling6, cropping patterns and crop production7.

Further, the patterns of heavy and violent rainfall, as captured by the rainfall erosivity factor, influence hydrological and erosive processes and as such are essential for the definition of soil and water conservation practices in the adaptation of agriculture to climate change8. Rainfall erosivity is one of the most important input parameters for describing erosive processes and proposing conservation measures by using soil erosion prediction models. Since soil erosion is difficult to measure at large scales, models are required for estimating soil loss by water erosion at regional, national and continental scales. Large scale and global model predictions are of utmost importance, since soil erosion is, in addition to soil sealing, the major threat to soil sustainability and consequently to water- and food security.