A Statistical Downscaling Framework for Urban Climate Adaptation: Projections and Implications for Tourist Activity in the City of Alicante (Spain)

Climate change poses significant challenges for Mediterranean coastal cities, where impacts on temperature and precipitation extremes are already evident. Effective urban adaptation requires high-resolution, local-scale climate information that gridded global climate models (GCMs) cannot yet directly provide. This study aims to develop and apply a statistical downscaling framework that translates coarse-resolution CMIP6 projections into actionable, local-scale climate information for the city of Alicante (Spain), with a particular focus on temperature and precipitation extremes relevant to urban planning and tourism. To this end, the LARS-WG stochastic weather generator was used to downscale two CMIP6 GCMs (HadGEM3-GC31 and MPI-ESM1-2) under SSP2-4.5 and SSP5-8.5 scenarios. Three future periods (2031–2050, 2051–2070, 2081–2100), referenced against the 1985–2015 baseline, were analyzed, focusing on key variables for urban tourism and infrastructure: temperature extremes (tropical and torrid nights), precipitation intensity, and dry spell lengths. Projections show a robust increase in nocturnal heat stress, with tropical nights (Tmin ≥ 20 °C) rising from 67 to 110 days/year by 2100 under SSP2-4.5, and torrid nights (Tmin ≥ 25 °C) from 1 to 24 days/year. Although with greater uncertainty, simulation of extreme daily rainfall is projected to increase by up to 65% in the short-to-medium term (2031–2050), contrasting with a relative long-term lengthening of dry spells. These findings provide a solid basis for an urban adaptation roadmap, highlighting critical needs for tackling nocturnal heat, flash flooding, and water management to safeguard key economic sectors such as tourism.