Does the SD GCM tool do bias correction of the downscaled data or does it require further bias correction methods?

Common approaches is used as the application of downscaling techniques, which are typically either dynamical or statistical (empirical). Statistical downscaling methods usually establish a statistical relationship between a local variable (predictand) and a larger-scale variable modelled by the global or regional model (predictor). There are a vast number of statistical downscaling techniques used in impact studies, and for recent overviews we refer to Fowler et al. (2007) and Maraun et al. (2010). Dynamical downscaling, where a regional climate model (RCM) is forced with boundary conditions from a GCM.

Teutschbein and Seibert (2012) have mentioned that several bias correction methods have been developed to downscale climate variables from climate models (Johnson and Sharma, 2011). These methods range from simple scaling approaches to rather sophisticated methods employing probability mapping or weather generators. Also, they have used different techniques of bias corrections method, including Linear Scaling, Power transformation, Distribution mapping, Local intensity scaling (LOCI), and Delta method.

Sachindra et al. (2014) stated bias-correction techniques could be applied not only to GCM outputs but also to the outputs of downscaling models, irrespective of whether the downscaling approach is dynamic or statistical. Also, Seibert (2012) used multiple bias-correction techniques (linear scaling, local intensity scaling, power transformation, variance scaling, quantile mapping, and delta approach) on dynamically downscaled precipitation and temperature.

Miao et al. (2016) assessed that in the simplest and widely used bias-correction technique, the future climate projection is adjusted merely by the delta change (DC) in the reference period (also called the "delta method").

Other researchers as Wetterhall et al. (2012), Hagemann et al. (2011), Fowler et al. (2007), Chen et al. (2013), Gutjahr and Heinemann (2013), Boe et al. (2007), and several others have been stated that BC is one of the statistical downscaling's approaches.

However, In the SD GCM software tool, you apply three methods of downscaling on a daily scale, including Delta, Quantile, and EQM. So, with selection one of them after the sets of processes, you can evaluate the mentioned method in monthly scale.

Wang et al. (2015) has stated that empirical statistical downscaling methods are classified based on calibration strategies (bias correction (BC) and change factor (CF)) and statistical transformations (mean based, variance based, quantile mapping, quantile correcting and transfer function methods). Frequently applied simple downscaling approaches include delta-change (DC) and bias correction (BC) methods. The former modify observations by the model-derived climate change signal (CCS) between the historical calibration and the scenario periods, whereas the latter correct raw model output, using model biases derived in the calibration period. Quantile mapping (QM) is a distribution-based BC method that removes quantile-dependent biases. If the observations, against which QM is calibrated, represent the same spatial scales as the raw climate model output (i.e. mean conditions over grid cells), QM does a mere BC.