A statistical approach to downscaling of sub-daily extreme rainfall processes for climate-related impact studies in urban areas

Abstract

This paper presents a spatial-temporal downscaling approach to describe the linkage between large-scale climate variables for daily scale to annual maximum (AM) precipitations for daily and sub-daily scales at a local site. More specifically, the proposed approach is based on a combination of a spatial downscaling method to link large-scale climate variables as provided by General Circulation Model (GCM) simulations with daily extreme precipitations at a local site and a temporal downscaling procedure to describe the relationships between daily extreme precipitations with sub-daily extreme precipitations using the scaling General Extreme Value (GEV) distribution. The feasibility of the proposed downscaling method has been tested based on climate simulation outputs from two GCMs under the A2 scenario (HadCM3A2 and CGCM2A2) and using available AM precipitation data for durations ranging from 5 minutes to 1 day at 15 raingage stations in Quebec (Canada) for the 1961–1990 period. Results of this numerical application has indicated that it is feasible to link large-scale climate predictors for daily scale given by GCM simulation outputs with daily and sub-daily AM precipitations at a local site. Furthermore, it was found that AM precipitations at a local site downscaled from the HadCM3A2 displayed a small change in the future, while those values estimated from the CGCM2A2 indicated a large increasing trend for future periods.