# Application of Machine Learning Techniques to
## 1 Introduction  
Climate change has emerged as a major driving force behind Pakistan's flooding over the past several decades, mainly by affecting snow melting and interfering with summer monsoon patterns [1]. Previous studies have also shown a growing interest in an accurate and calculated evaluation of climate change that can provide a rational system to adapt to a rapidly changing environment. Climate change impact analyses use long records of climate data from numerous gauging sites to estimate the historical and projected climate change trends. There is a growing institutional and social need to predict the potential impacts of climate change and the damage due to floods and droughts owing to spatio-temporal variability in the climate, specifically the rainfall. Previous studies have established that spatio-temporal dynamics in atmospheric covariates are strongly linked to the precipitation characteristics on a regional scale. The atmospheric circulation covariates are important in defining the precipitation patterns across the region [2]. Different atmospheric oscillations are responsiblefor this variability, which has been characterized by various climate models/simulations, including several parameters related to atmospheric oscillation moisture and wind fluxes [3]. The driver of seasonal rainfall in southeast Asia is the Monsoon circulation [4] and increases in the probability and severity of extreme events (e.g., droughs, heatwaves and floods) have been documented in several studies [5; 6]. The spatio-temporal variability of the hydrological cycle parameters affects the social and environmental sustainability [7]. Therefore, the patterns and projected trends of precipitation, which is the most important variable of the hydrological cycle varies spatially as well as temporally, must be studied. Simulation results from general circulation models (GCMs) are used to study the implications of hydro-climatic change and are considered the most comprehensive and valid instruments, with the ability to reproduce climate variables in chronological order as well as yield future projections using scenarios [8; 9; 10; 11] recommended by the intergovernmental panel on climate change (IPCC). Numerous outputs from GCMs are available to gain insight into climate processes and the impact of various scenarios on these processes [12; 13]. The question arises as to how to use these climate simulations to obtain meteorological inputs for impact models to characterize all the uncertainties involved in the models. Previous studies have reported attempts to investigate the suitability of climate models when simulating different climate parameters, such as precipitation [14]. spatio-temporal distribution by the GCM is an important aspect that must be considered for the selection of the model [15]. Further discussion regarding the selection of appropriate GCMs is presented in Section 3.2. Climate model simulations should have adequate precision to assimilate crucial information in the observational data, which forms the basis to forecast and more efficiently predict climatic conditions. The sources of uncertainty in climate simulations and predictions are (1) model equations parameterization and initial conditions associated with chaotic systems [16]; (2) the variability in the predicted scenario caused by anthropogenic forcing, which is unknown in the future; and (3) climate variability [17]. To account for these uncertainties, multiple GCM assessment studies analyze model efficiency at simulating the dynamic and thermodynamic variables and the environmental variables related to atmospheric chemistry [4; 18; 19]. The common trend among the climate research community, however, is the statistical performance evaluation of GCM using climate output variables from its simulations [7; 19; 20; 21; 22; 23].