Comparison of statistical downscaling methods for climate change impact analysis on drought

Abstract

Abstract. General circulation models (GCMs) are the primary tools to evaluate the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Besides the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small and large scale variables. This study compares four statistical downscaling methods (SDMs) of bias correction (BC), change factor of mean (CFM), quantile perturbation (QP) and event based weather generator (EBWG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000. A set of drought related aspects is analysed: dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 14-member ensemble of CMIP6 GCMs, each powered by four future scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability of the climate. While all methods show a good agreement on downscaling total precipitation, the CFM method fails to downscale dry day frequency well. The QP method outperforms the others in downscaling dry spells. Using this method, dry day frequency is projected to increase significantly in the summer months, with relative changes of up to 20.4 % in the worst-case climate change scenario. At the same time, total precipitation is projected to decrease significantly by up to 41.9 % in these months. Lastly, extreme dry spells are projected to increase in length by up to 7.4 %.

Statistical downscaling of precipitation using inclusive multiple modelling (IMM) at two levels

“…The monthly time scale for precipitation downscaling was investigated in the study, but other downscaling resolutions are practised: a higher resolution of 3 h was used by Mendes & Maia (2020); the daily interval was used by various authors (see Table 1) and lower resolutions of the monthly intervals (see Table 1) or annual interval by . Generally, higher resolutions are appropriate to investigate hydrological models such as rainfall-runoff (e.g., Tavakolifar et al 2017), whereas lower resolutions are used for groundwater (e.g., Tewari et al 2015) or drought studies (e.g., Tabari et al 2020). Considering the importance of downscaling resolution, there is room for investigating the applicability of IMM for downscaling and projecting precipitation at different time resolutions.…”

Extreme value analysis dilemma for climate change impact assessment on global flood and extreme precipitation

“…This deficiency leads to a large uncertainty in extreme precipitation projections, especially where and when local scale processes to produce extreme precipitation are dominant (Tabari et al, 2019), since GCM uncertainty is one of the main uncertainty sources (e.g., Kay et al, 2009;Hawkins and Sutton, 2011;Tabari et al, 2019;Xu et al, 2019). The extreme precipitation simulations of GCM can be improved by dynamical (Meredith et al, 2018) and statistical (Tabari et al, 2020) downscaling, which needs further investigation in the future.…”