On the Use of Statistical Downscaling Time-invariant Transfer Functions and Their Effect on Historical and Future Climate Indices. Case study: Heat Wave Duration in Montreal, Canada

Statistical downscaling techniques are often used to generate finer scale projections of climate variables affected by local scale processes not resolved by coarse resolution numerical models like global climate models (GCMs). Statistical downscaling models rely on several assumptions in order to produce finer/local scale projections of the variable of interest; one of these assumptions is the time-invariance of the relationships between predictors (e.g. coarse resolution GCM output) and the local scale predictands (e.g. gridded observation based time-series or weather station observations). However, in the absence of future observations, statistical downscaling studies use historical data to evaluate their models and assume that these historical simulation skills will be retained in the future. Here we study the effect of this generally overlooked assumption when downscaling daily maximum temperatures and using the downscaled information to estimate historical and future heat waves duration indices (HWDI) over Montreal, Canada. To do so, we used regional climate model (RCM) output from the Canadian RCM 4.2, as proxies of historical and future local climates, and daily maximum temperatures obtained from the Canadian GCM 3.1. The results show that the root mean squared errors (RMSEs) between the pseudo-observations and the statistically downscaled time-series (historical and future) varied over time, with higher errors in the future period. The results also show the effect of different post-processing techniques like randomization and variance-inflation on the tails of the statistically downscaled time-series.35 on 8-10-2015-->