The Amazon Rainforest is a dynamically intricate hotspot region with high biodiversity and importance to the global hydrological and carbon cycles. Over the last decade, the frequency of extreme events in the Amazon has increased due to climate change. This study presents a brief background overview of the causes and impacts of the Amazon droughts of 2005 and 2010 based on past and current studies in literature. This study also reports the analysis of the performance of 34 fully coupled global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a Community Land Model 4 (CLM4) run to simulate current seasonal cycles of precipitation, temperature, leaf area index (LAI), surface runoff, and aboveground biomass stock against observational datasets. The land and atmospheric model variables of interest include precipitation, temperature, leaf area index, carbon storage in vegetation, net primary production, total runoff, total surface runoff, and total soil moisture content. The present day climatology obtained from CMIP5 historical runs is 1980-2005, and future climatology from 4 representative concentration pathway scenarios (RCPs 2.6, 4.5, 6.0, and 8.5) is 2075-2100. All model outputs are monthly means from the r1i1p1 ensemble. The seasonal and interannual means extracted from the variables are analyzed to compare against observational data to evaluate model performance. Model variability index (MVI) was calculated to compare each model's variability in the North and South Amazon grid boxes to assess the standard deviation difference between model and observed datasets to identify biases in each model. MVI values differ among variables and location of the Amazon. Results also show that models were able to reproduce seasonal and annual cycles of precipitation in the Amazon better than other observed data. Two types of skill scores were used to rank models to provide comparison to the seasonal and interannual variability in observed data. The root mean square error (RMSE) statistical approach is used to check the model's ability to reproduce both the phase and amplitude of the observations during the climatology period and account for the errors in the spatial pattern and annual cycle. The probability density function (PDF) approach compares the common area under the PDF curves based on Epanechnikov kernel smoothing to evaluate the ability of the model to reproduce both the mean state and interannual variability of a variable. Poor model simulations are close to 0, and perfect model simulations are close to 1. The metrics in this study found no significant correlation between current skill scores and future projections of climate variables. However, correlation studies between variables suggest good relationship between temperature, precipitation, and LAI in models. Future changes in RCP 8.5 show overall decreases in precipitation and increases in temperature, surface runoff, soil moisture, and carbon stock, although uncertainty remains to the exact fate of the Amazon towards the end of the century. ii