Intensity-duration-frequency (IDF) curves require accurate observations which are not available everywhere. To provide globally consistent IDF maps, we harness the accuracy of Global Sub-Daily Rainfall (GSDR) gauge observations and combine this with the power of a random forest regression model to regionalise the parameters of a the SMEV (Simplified Metastatistical Extreme Value) distribution. After regionalisation, it is possible to compute intensities for any combination of return period and durations up to 24 hours. These regionalised intensities are named BURGER, the ”Bottom Up Regionalised Global Extreme Rainfall dataset”. Comparing intensities from BURGER against those obtained at GSDR stations shows that errors increase with less frequent events. Median percentage biases range between -20 % and 35 %, with a median around 0 %, yet with marked regional variations. Despite results indicating a too light tail, their agreement with expected intensities is still good. Intensities from simulations excluding station data in the UK and Germany deviate up to 15 % from those obtained with the station data included. A benchmark with the remote sensing-based GPEX dataset did not reveal structurally lower agreement in ungauged regions compared to gauged regions, suggesting the transfer to ungauged areas works reasonably well. Comparing results with NOAA data shows that different data and methodologies can hamper a robust benchmark: while at some GSDR stations NOAA data agrees with BURGER data, it hardly agrees with empirically derived intensities at other stations. This first bottom-up approach to global IDF data yields promising results and insights warranting future improvements.