## 1 Introduction  
The variability in precipitation has a significant impact on the agriculture-based Indian economy [1]. The impact of climate change on hydrological processes at a regional level or watershed scale has been noted by the Intergovernmental Panel on Climate Change (IPCC) [2]. The importance and influence of General Circulation Models (GCMs) are widely known for climate change scenarios, providing climate forecasts and dealing with unproitious climate variability. The datasets related to atmosphere and meteorological parameters provided by GCMs simulations are good for studies to some extent, but their coarser spatial resolution restricts their application at the regional level [3]. Hence, a relatable climate prediction is essential for adaptation, mitigation, and policymaking for agriculture, the environment, and water resources. Variations in precipitation and temperature are considered significant driving parameters due to their pronounced influence on regional hydrological processes [4; 5]. Historical and future changes in the meteorological variables at global and regional scales have been increasingly studied using daily weather datasets and GCM simulations [6; 7; 8; 9; 10]. Recently, the development of various bias correction methods has led to their extensive use in downscaling the authentic climate projection data of coarser-resolution at the regional level [11]. The biases like overestimation or underestimation of rainfall and increased rainy days with lesser rainfall intensities are present in the simulated datasets. The simulated outputs of hydro-meteorological variables such as temperature, precipitation, and runoff with biases affect climatological and hydrological analyses when utilized directly for impact studies [12]. Hence, to obtain the authentic climate projections from GCM outputs, it is necessary to perform bias correction on the GCM outputs. Bias correction is the statistical adjustment of GCM outputs towards the actual climatology. Bias correction methods commonly assume that there is a statistical relationship between the models' simulated raw data and observed data that will continue in the future [13; 14]. Despite the fact that bias correction must be performed while using GCM outputs as inputs for various hydrological models, there is still uncertainty in the bias-corrected results because of different bias correction techniques [15; 16; 17]. Bias correction methods are developed to reduce biases and rectify the simulated data produced by GCM models. Some of these techniques are simple scaling techniques and some are refined techniques such as distribution mapping techniques [11]. The scaling techniques generally take account of linear or nonlinear relationships of climatic factors to adjust the differences between observed and GCM data, i.e., linear or nonlinear formulation. The scaling techniques include the linear scaling method (LS) [18; 19], local intensity scaling (LIS) [20], and the power transformation method (PT) [21]. The distribution mapping (DM) technique involves distribution-based and distribution-free mapping methods that utilize the statistical distribution of GCM-simulated data to the distribution of observations. The DM technique assumes that the GCM data follows a certain distribution like the Gamma distribution and Gaussian distribution [22; 23]. The maximum area of the Upper Bhima sub-basin is a rainfall shadow zone/water scarcity zone and, hence, an evaluation and performance assessment of the bias correction methods is essential here in order to study the impacts of climate change on hydro-meteorological variables. The objective of the study is to analyze and evaluate bias correction methods applied to the precipitation, maximum temperature, and minimum temperature of the Coupled Model Intercomparison Project Phase 5 (CMIP5) of the ARC Centre of Excellence for Climate System Science (ACCESS1-3) historical model output in the Upper Bhima sub-basin in Maharashtra, India.