Toward a regional-scale seasonal climate prediction system over central Italy based on dynamical downscaling

Anticipating seasonal climate anomalies is essential for defining short-term adaptation measures. To be actionable, many stakeholders require seasonal forecasts at the regional scale to be properly coupled to region-specific vulnerabilities. In this study, we present and preliminarily evaluate a regional-scale Seasonal Forecast System (SFS) over Central Italy. This system relies on a double dynamical downscaling performed through the Regional-scale Climate Model (RCM) RegCM4.1. A twelve-member ensemble of the NCEP-CFSv2 provides driving fields for the RegCM. In the first step, the RegCM dynamically downscales NCEP-CFSv2 predictions from a resolution of 100 to 60 km over Europe (RegCM-d1). This first downscaling drives a second downscaling over Central Italy at 12 km (RegCM-d2). To investigate the added value of the downscaled forecasts compared to the driving NCEP-CFSv2, we evaluate the driving CFS, and the two downscaled SFSs over the same (inner) domain. Evaluation involves winter temperatures and precipitations over a climatological period (1982-2003). Evaluation for mean bias, statistical distribution, inter-annual anomaly variability, and hit-rate of anomalous seasons are shown and discussed. Results highlight temperature physical values reproduction benefiting from the downscaling. Downscaled inter-annual variability and probabilistic metrics show improvement mainly at forecast lead-time 1. Downscaled precipitation shows an improved spatial distribution with an undegraded but not improved seasonal forecast quality.