Building the scientific basis for the adaptation strategy of an Alpine region: The ‘State of the climate in Trentino’ report

Many regional administrations are currently developing tailored adaptation strategies, based on the unique characteristics of their territories, which includes local climate patterns and, more importantly, environmental and socioeconomic factors. Yet, exploiting this wealth of information to design effective climate strategies remains a challenge. This study outlines the approach used to establish the basis for the climate strategy in the Autonomous Province of Trento, a mountainous region in northern Italy and part of the European Alps. The approach adopted by the regional government emphasizes the need for a multi-level, multi-stakeholder approach to shape the development of a regional adaptation strategy. Here, we present three major steps of this process, highlighting the collaborative effort between diverse entities: (1) Compilation of an up-to-date scientific review of the existing knowledge about climate change impacts in the region, (2) Creation of reference climate scenarios, and (3) Dissemination of a summary of the report to the general public. The literature review revealed large heterogeneity across socioeconomic sectors in the scientific literature focusing on the impacts of climate change. This disparity was reflected in the informative report, which simplified and summarized the technical information without sacrificing scientific accuracy. The creation of climate scenarios had to balance multiple aspects, including mountain topography, data availability, and needs from stakeholders, by using a layered approach combining spatially aggregated results, statistical downscaling, and high-resolution convection-permitting models. • Widely accessible state-of-the-art climate information obtained through the collaboration of research and local institutions. • Overview of the scientific literature availability on climate change impacts over the Trentino territory. • Benefits and drawbacks of climate scenarios using dynamic and statistical downscaling