This chapter provides a thorough summary of many research efforts that examine the patterns of multivariate precipitation attributes and the different approaches and tools employed in India and worldwide. The time and spatial scale are essential factors in identifying the patterns and the impact of climate change. The statistical analysis of a time series data set involves numerous approaches, such as Innovative Trend Analysis (ITA), a non-parametric statistical Mann-Kendall (MK) test, and the utilization of Copulas to investigate the correlation between different parameters. Moreover, wavelet analysis is employed to break down signals in order to evaluate trends over a period of time. Recent studies suggest an increasing utilization of sophisticated techniques such as artificial neural network (ANN) and machine learning (ML) to improve comprehension of multivariate rainfall attributes. Comprehensive investigations require analysing various factors such as average rainfall, variability, distribution characteristics, extreme rainfall events, and trends. This involves assessing statistical parameters such as mean, standard deviation, coefficient of variation, coefficient of skewness, and coefficient of kurtosis. Furthermore, we demonstrate the multivariate rainfall characteristics by utilizing the generalized extreme value (GEV) distributions to examine the maximal attributes of monsoon rain periods. The selected multivariate rain spell attributes were determined by utilizing maximum likelihood estimation (MLE), such as maximum volume (Vmax), peak intensity (Rmax), and maximum duration (Dmax). The performance assessments involved comparing theoretical and empirical quantiles to determine the accuracy of the distributions in representing intense rain spell features. Thus, an in-depth investigation of rainfall attributes aims to employ cutting-edge and accurate methodologies to enhance understanding of rainfall dynamics in any given location.