A temporal downscaling approach for sub-daily gridded extreme rainfall intensity estimation under climate change

Study region: Canada. Study focus: Intensity-duration-frequency curves need to be updated to account for the potential effect of climate change. However, sub-daily regional climate model (RCM) simulations required for this purpose are commonly unavailable. The scaling approach is often applied for temporal downscaling of point daily precipitation; nevertheless, difference in spatial scale between simulations and observations may complicate its application at the grid scale. This study proposes a grid-scale temporal downscaling approach for estimating sub-daily gridded extreme rainfall intensity under climate change at regions with a relatively high station density. The approach uses daily annual maximum rainfall intensity series from RCM simulations and grid-scale scaling properties from observations. The study evaluates two methods for estimating gridded scaling exponents needed in temporal downscaling. The assessment is performed on observation-based and RCM-based temporally downscaled quantiles. The effect of temporal downscaling and bias correction on sub-daily quantiles and relative changes is also analysed. The approach is applied to three case studies across Canada. New hydrological insights for the region: Piecewise simple scaling is found over the study domains, indicating caution in using simple scaling. The performance of the approach in estimating subdaily observation-based gridded rainfall intensity quantiles is suitable, with preference for point to grid transfer at the annual maximum series stage. Bias correction is necessary for properly estimating sub-daily RCM-based quantiles, presenting less impact on relative changes.

Automated summary

This study proposes a grid-scale temporal downscaling approach for estimating sub-daily gridded extreme rainfall intensity under climate change in regions with a relatively high station density. The assessment evaluates two methods for estimating gridded scaling exponents needed in temporal downscaling and analyzes the effect of temporal downscaling and bias correction on sub-daily quantiles and relative changes. The study also finds piecewise simple scaling over the study domains and emphasizes the importance of bias correction for estimating sub-daily RCM-based quantiles.