SD GCM is used in this papers:

1- Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment

2- Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through Genetic Algorithm in Punjab, Pakistan

Downscaling through four methods and generating GCM database

Many of researchers and users need a software package that it can easily use statistical downscaling models. The SD GCM (Statistical Downscaling of General Circulation Models) is a useful tool for downscaling CMIP5 models under RCPs Scenarios. There are a numerous number of statistical downscaling (SD) methods. In this tool there are four statistical downscaling models: the Delta, the Quantile Mapping (QM) (Panofsky and Briar, 1968), the Empirical Quantile Mapping (EQM) (Boe et al., 2007) and a new method that is called FlashSort (FS).

In this package user can make a database for applying every CMIP5 model under every RCP scenario. SD GCM with manual data entry of input values and other details are to be provided as a separate ﬁle. All CMIP5 models have an especial format in the name of models. Every model has developed by a special company, and every company produce an individual name. Therefore, there is a challenge to read the name of files by the software for every model. In this software, the user can easily browse the desired CMIP5 model for a specific variable.

There is an option in the SD GCM software for evaluation data of models. In this step the user can assess the ability of the CMIP5 model with observation data, in a common period. The observation data would be in Excel format file and the order of data in the columns are not important, therefore user can easily load the input observation data without any concerns. There are six efficiency criteria for evaluation phase: Pearson Correlation, Nash-Sutcliffe efficiency, Spearman Correlation, RMSE (Root Mean Squared Error), d (index of agreement), and MAE (Mean Absolute Error).There are a complete help file in this package that describes all setup steps.

Raw outputs from General Circulation Model (GCM) simulations are inadequate for assessing the impact of hydrological, agricultural, and other studies. Due to inadequate and too coarse spatial scale of GCMs outputs (typically 250 km), scientists have to perform different methods to solve this problem, and therefore, downscaling methods have been used. Downscaling bridges the gap between coarse and fine scale climatic data. Downscaling can be performed on spatial and temporal aspects of climate projections. Spatial downscaling refers to the methods used to extract finer-resolution spatial climate information from coarser-resolution GCM output, e.g., 500 kilometers grid cell GCM output to a 20 kilometers resolution, or even a specific location.

There are broadly two types downscaling: Dynamical Downscaling (DD) and Statistical Downscaling (SD) (Christensen et al., 2007). DD, nesting a ﬁne scale climate model in a coarse scale model, produces spatially complete ﬁelds of climate variables. DD is very computationally intensive, making its use in impact studies limited, and essentially impossible for multi-decade simulations. DD models are very complex and requires substantial computational resources, often at same level as required for GCM simulations. The implementation of these models is prone to error. DD involves a regional climate model (RCM) used to model the target region at finer scales bounded by larger GCM nodes.

SD (Statistical Downscaling) methods don’t require significant computational resources and they can easily run by a simple computer with rely on simple regression analyses in a flash time, so, due to the ease of their implementation, these methods have high opportunity that will select from related users. A vast number of techniques have been developed for SD and these are based on the determination of statistical relations between large-scale synoptic predictors and local observations from ground stations. SD can produce site-specific climate projections, which DD cannot provide since they are computationally limited to a 20–50 kilometers spatial resolution. One advantage of SD techniques is that they are less computationally intensive and hence can be used to downscale many GCM (or RCM) climate projections. Furthermore, compared to DD methods, the SD method is relatively easy to use and provides station-scale climate information from GCM-scale output.

In general, the statistical methods can be divided into three categories: regression (transfer function) method (e.g. Kang et al. 2007), stochastic weather generator (Richardson 1981) and weather pattern schemes. There are a numerous number of statistical downscaling methods. One of the most popular and common of them is Bias Correction (BC) that has been applied extensively for impact assessment and employed in climate change studies in all over the world (Wood et al., 2002; Payne et al. 2004). One of the best references for review of different kind of BC approaches can be found in Themeßl et al. (2012).

These methods include of many statistical technique that is run with various application in every part of the world, but almost many of these applications with CMIP5 outputs are difficult to run by end-users and none of them don’t have an exe file that can easily run. Because of this weakness, user-friendly softwares are vital in order to ease the downscaling process for end users. SD GCM software can perform four different SD methods.

This tool can work just with daily station and GCM data