# Application of Machine Learning Techniques to
## 1 Introduction
These climate variables are averaged temporally and spatially on various scales for the assessment of the GCM's ability to either simulate historical data or to present the range of climate output variables to detect a plausible future [22; 24].The majority of assessment studies of climate change used the following two approaches to evaluate the climate model skill to represent past observational climate data:  
1. comparing the spatio-temporal average of historical/baseline observational and GCM data of different climate variables [22; 25; 26], using several performance indicators [27; 28; 29]; however, changes in the spatio-temporal average variable values may not represent the change in extreme values [30]. 2. comparing the observational and GCM data at each grid point [21; 31; 32] and then selecting the GCM in the entire study area using different filtering approaches, such as clustering hierarchy [33; 34], Bayesian weighting [35], weighted skill score [15; 36] and spectral analysis [37]; however, such selection processes do not cater to the area-specific efficiencies of the climate models because this efficiency varies amongst models and regions [4; 27]. Several studies [21; 31; 32] have shown that the evaluated GCMs only account for some grid points over the entire study region. However, these studies have overlooked the ability of GCM to reproduce spatial climatic patterns. The drivers of this spatial variability are atmospheric circulation patterns that depend on the geographical location of the area. The capacity of the GCMs to reproduce the patterns of climate parameters spatially, in the baseline/historical period is, therefore, imperative [7; 21]. The GCM or ensemble of GCMs selected, via the aforementioned comparative analysis of spatio-temporal averages and filtering techniques, may be appropriate for simulating the spatial climatic patterns for some percentage of the entire study area but not all, thus enhancing the uncertainties in projecting the climate. To narrow this uncertainty in projecting the climate variables (precipitation) trends, we propose a different method of the selection of GCMs by first identifying the climatologically homogeneous zones and then selecting the GCM in each zone through past performance assessment. These homogeneous sub-regions are based on similar spatial and temporal climate patterns, as depicted by the highly resolved observation data. Following the philosophy of [PERSON] et al. [4] for the regional suitability of the GCMs, we selected the GCMs in every homogeneous precipitation region and validated our selection through the comparative analysis with Simple Composite Method (SCM). The SCM is broadly used for generating the multi-model ensemble that is, to obtain the equally weighted mean of all the ensemble members data at each grid point [7]. With the support of machine learning techniques, long records of climate data, from numerous gauging sites and web sources, can be easily analyzed and used to determine historical and projected trends of climate change. In the present study, the modules using machine learning techniques were developed throughout the various steps of the framework. The framework comprises five important steps: (1) developing homogeneous precipitation zones using highly resolved observation data; (2) the selection of the best suitable GCM for each zone based on the estimated correlation coefficient using the GCM data and highly resolved observed data in every homogeneous climatic region; (3) comparison and validation of the selected GCM; (4) sampling the daily precipitation data for the projected period (2006-2099); based on the forcing scenarios and (5) performing future precipitation trend detections. The outcomes of this framework are the estimated divisional boundaries of the climate zones, suitable GCMs for the climate impact studies and seasonal rainfall trend projections.