# Application of Machine Learning Techniques to
## 3 Methodology
### GCM Selection  
The criteria used to select the GCMs are the availability of the latest generation of GCMs, good spatial resolution, past performance of GCM to replicate the historical data and the representativeness of the GCM for a wide range of climatic variable (precipitation) projections [7]. The most commonly adopted criteria are the assessment and selection of GCMs based on their capability to simulate the historical and present climate, which have been adopted by numerous studies [8; 9; 10]. An efficient and sensible selection of GCMs is required to generate reliable and diverse meteorological inputs for the impact models. Most previous studies show that a past performance assessment is among the most effective methods for the selection of GCMs, as the GCMs thus selected can be better predictors of future climatic conditions [31]. Several selection methods have been reported in the literature. [PERSON] et al. [71] extracted GCM data at four points in the vicinity of observation stations and performed a comparative analysis between the GCM and the observed data. They used the averages of the time-series data at each station for their analysis. [PERSON] et al. [72] selected GCMs using an average of the climate parameters for the entire study area in the Zhejiang Province of Southeast China. [PERSON] et al. [32] ranked the GCMs at every grid point after re-gridding the GCM data at the same spatial resolution, comparing the data with the gridded data product of the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRDITE) for GCM performance evaluations in all of Pakistan. When comparing the best GCM with the highly ranked GCM at each grid station, they found a significant difference between the two with respect to precipitation. [PERSON] et al. [15] analyzed the climate models from the Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC) for two regions in the Murray Darling Basin, divided based on rainfall classification. For different climate variables, they demonstrated that the models that are flawed in one region may be better for another region; model selection should be area specific. They calculated the skill scores associated with the different climate models in the study region. [PERSON] et al. [22] averaged the climate data over \(2.5^{\circ}\times 2.5^{\circ}\) grid cells in three river basins (i.e., the Indus, Brahmaputra and the Ganges) and presented their model with the selected GCMs. [PERSON] et al. [73] also suggested certain Coupled Modelled Intercomparison Project 5 (CMIP5) GCMs after evaluating their performance using seasonal rainfall spatial correlations in the Indo-Pak region. [PERSON] et al. [7] evaluated the spatial accuracy of the CMIP5 GCMs using different spatial metrics for all of Pakistan. They performed the analysis after re-gridding the GCM data into a \(2^{\circ}\times 2^{\circ}\) grid size. [PERSON] et al. [31] ranked the GCMs for the maximum and minimum temperatures across India. This framework comprised the evaluation of GCM suitability at every grid point and then ranking the GCMs using compromised programming. In the present study, after defining the clusters of stations with homogeneous precipitation, one representative station was selected in every climate zone, using quota sampling [61]. The selection of the representative station was done based on the average climate signal climate signals (Component Scores) in a respective climate zone. The comparative analysis of GCMs data and APHRODITE data at the representative station of each climate zone was accomplished using the coefficient of determination (R\({}^{2}\)). A reduced correlation existed within the daily outputs of the GCMs and the in situ data. However, better correlations were obtained when seasonal data in the GCMs were compared with the seasonal observation data.