Extreme event attribution aims at evaluating the impact of climate change on extreme events. In this work, we adapt an attribution methodology applicable to extratropical cyclones, and test it on storm Alex: an explosive extratropical cyclone that affected Southern France and Northern Italy at the beginning of October 2020. The methodology specifically combines atmospheric analogues, dynamical systems theory and extreme value theory. We first divide 6-hourly ERA5 data into two periods: a counterfactual period with a weak climate change signal (1950-1985) and a factual period with a strong climate change signal (1985-2021). We then identify the 30 cyclones in each period whose sea-level pressure maps were most similar to Alex's map. We term these analogues of Alex. We find that analogues in the factual period are more persistent than in the counterfactual period, which may favour severe impacts resulting from persistent strong winds and heavy precipitation, as was the case for Alex. This effect is compounded by the doubling in accumulated daily precipitation detected in Northern Italy between the counterfactual and factual analogues. In the factual period, the analogues also display deeper cyclones with poleward-shifted backward trajectories. Furthermore, we identify a seasonal shift of the analogues, from spring to autumn. Finally, the analogues in the factual world are closer to Alex than in the counterfactual. These changes collectively point to high-impact storms like Alex becoming more common in a changing climate.