Climate change is intensifying extreme rainfall and flash floods across the arid Arabian Peninsula (AP). Adaptation requires a large ensemble of high-resolution precipitation projections obtained from dynamical downscaling, which remains computationally prohibitive. Here, we present the first component of a statistical-dynamical downscaling framework designed to reduce this computational burden. Machine learning models were trained to identify convective environments (CEs) using predictors derived from ERA5 reanalysis and a binary predictand from IMERG precipitation data. The best-performing model was applied to the CMIP6 ensemble to assess CE occurrence and components under current and future climates. At the current regional warming level, CEs are less frequent in the CMIP6 ensemble relative to ERA5, accompanied by drier and more stable environments, suppressed updraft range, and stronger wind shear. At an additional +1 °C regional warming, CE occurrence generally increases, excluding spring, accompanied by a diurnal shift towards nocturnal and morning periods. A general decrease is projected at an additional +3 °C, excluding winter. Nevertheless, CE-related moisture and instability exhibit monotonic increases with warming, suggesting more extremes and a shift toward a higher contribution of extremes to total rainfall. The resulting CE probability dataset enables targeted event selection for convection-permitting dynamical downscaling across the AP.