Projecting the impacts of climate change requires large ensembles of climate variables that match historical observations, align with the warming ranges assessed by the IPCC, and can efficiently run new future emissions scenarios, including the newest generation of climate model scenarios (CMIP7) and pathways consistent with countries’ Paris Agreement pledges. Generating such ensembles at the scale needed for impact studies is normally computationally prohibitive. We close this gap with GCMagicc, a hybrid model that pairs a simple physical climate model with machine learning to generate ensembles of 10 climate variables at the resolution of full-scale Earth system models, without relying on GPU resources or retraining for new scenarios. Being trained on 32 CMIP6 Earth system models and observational/reanalysis data, GCMagicc complements rather than replaces Earth system models. We apply it to a range of future pathways: the canonical set of SSP scenarios in the latest Intergovernmental Panel on Climate Change (IPCC) report (1.2–6.1 °C warming, min-max range across scenarios of 5–95 percentile ranges), current policies (2.3–4.0 °C); national pledges under the Paris Agreement (1.5–3.3 °C) and the CMIP7 range from the so-called ’VL’ to ’H’ scenarios (1.2–4.2 °C), releasing a large public dataset. As an illustration of the usefulness of GCMagicc, we perform an attribution analysis on the severe 2025 Iranian drought using GCMagicc ensembles (and three CMIP6 ESMs large ensembles as comparisons) with and without anthropogenic forcings. Our GCMagicc results suggest a strong anthropogenic signal, with a median probability of drought conditions at least as severe as the observed ones of 29 % (median) with anthropogenic forcing and zero probability (median) under natural-forcing only simulations. In the future, drought conditions are projected to materially worsen, amplifying the potential for severe agricultural and food security impacts and geopolitical conflicts that use water scarcity as a weapon. GCMagicc data is available at <a href="https://gcmagicc.org" target="_blank" rel="noopener">https://gcmagicc.org</a>.