Site-specific projection of rainfall patterns under climate change by joint sparse representation

As climate change alters global rainfall patterns, many regions are facing increased intensity and frequency of rainfall events. These changes pose significant risks to civil infrastructure, which was often designed based on historical data and may no longer be resilient. Rainfall-induced failures can lead to severe, life-threatening consequences. Local factors, such as topography and elevation, greatly influence rainfall variability, making site-specific projections essential for effective risk assessment of infrastructure. However, current rainfall projections from General Circulation Models (GCMs) have coarse spatial resolutions (e.g. 100 km), which are inadequate for assessing risks at specific sites, such as slopes near railways, where the relevant scale is often tens to hundreds of metres. This study proposes an innovative method that integrates historical rainfall records with GCM projections using a joint sparse representation (JSR) framework to project future rainfall patterns at specific sites. This approach combines regional trends from GCMs with local data to maintain regional consistency while accurately reflecting local characteristics. A temporal downscaling step further enhances the resolution for engineering applications. The method is demonstrated using real rain gauge data from Hong Kong.