Projected Evolution of Climatic Aridity in Spain: Robust Signals and Model Uncertainties

ABSTRACT Spain's geographical position and climate characteristics make it highly vulnerable to climate change–driven projected aridity change. This study assesses the projected evolution of climatic aridity across mainland Spain, the Balearic Islands, and the Canary Islands throughout the 21st century. Using the UNEP Aridity Index ( AI = P / ETo ), we analyse an ensemble of high‐resolution CMIP6 simulations under four Shared Socioeconomic Pathways (SSPs) and four Global Warming Levels (GWLs), relative to the 1985–2014 baseline. The CMIP6 models exhibit a systematic dry bias, characterised by underestimated precipitation ( P ) and overestimated reference evapotranspiration ( ETo ) over the Spanish mainland, leading to artificially low historical AI values and excessively steep ETo trends. To mitigate this bias, we apply a delta‐change ( Δ AI ) method, which corrects the climatological baseline but does not eliminate process‐level uncertainties. Despite these limitations, the ensemble shows a consistent directional signal of increasing aridity across all regions and scenarios. The magnitude of projected aridity change scales with global warming and emissions intensity, reflecting a clear dose–response pattern. This trend is largely attributed to rising ETo , although its physical realism remains uncertain given the models' underestimation of precipitation and the use of empirical models to estimate ETo . Under high‐emission scenarios (SSP3‐7.0 and SSP5‐8.5), arid and semi‐arid zones expand across southern Spain, whilst the Canary Islands experience a pronounced shift towards hyper‐arid conditions. Overall, the results indicate that Spain is likely to face a progressive and spatially heterogeneous increase in climatic aridity, though the mechanistic basis of this change remains uncertain. These findings highlight both the directional robustness of projected aridity change and the need for more physically grounded model evaluation before fully translating these projections into adaptation strategies.