
This dataset provides convection indices or predictors derived from ERA5 over the Arabian Peninsula, covering the period 2001 - 2024 at 1 degree x 1 degree and a 6-hour interval. Additionally, the 6-hourly accumulated precipitation from IMERG V07 is included. The dataset includes raw (not bias-corrected) convective indices estimated from the historical and SSP5-8.5 experiments from CMIP6. However, only the years corresponding to the current regional warming level (+1.22°C above the preindustrial level) and to two future levels (+2.22°C and +4.22°C) are provided. The indices were computed using the R package MeteoMate and are provided in HDF5 format. Bias correction was applied to the CMIP6 indices using the MBCn algorithm from the R package MBC prior to estimating convection probabilities. The repository includes trained random forest (RF), extreme gradient boosting (XGB), and deep learning (DL) models for detecting or estimating the probability of convective environments. Additional information can be found in the README file. The dataset is intended to foster research on convective environments, extreme precipitation, convection-permitting modelling and climate change impacts over the region.
AcknowledgementsThe author gratefully acknowledges the computing time provided on the high-performance computer at the NHR Centre of TU Dresden. This centre is jointly supported by the Federal Ministry of Research, Technology and Space of Germany and the state governments participating in the NHR (www.nhr-verein.de/unsere-partner).
Convective environments, Machine learning, Convective indices, Arabian Peninsula, Climate change
Convective environments, Machine learning, Convective indices, Arabian Peninsula, Climate change
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