1. A method for change point detection in causal modeling in climate data, comprising: receiving data, {Y j,t , j=1,. .. , d; t=1,. .. , T} where Y j,t is a measurement taken for climate feature j at time t, in a time series 1. .. T; initializing Markov state variables {S t } t=1,. .. , T where S t =k if time point t is in state k wherein S t represents the state at time t; initializing Vector Autoregressive (VAR) coefficient θ ijk representing causal effects of feature i to feature j in state k; based on the received data, executing, by a processor, a two-step iterative Expectation-Maximization (EM) algorithm which incorporates Bayesian hierarchical group selection until the algorithm converges, the two-step iterative EM algorithm including an E-step for imputing missing data S t through backward-forward steps and assigning Markov states to each time points, and an M-step for updating parameters ψ and selecting causal effects through Bayesian hierarchical group Lasso, wherein ψ=(θ, σ 2 , P), θ represents the VAR coefficient, σ 2 represents variance of each Markov states, and P represents a transition matrix; detecting change points in climate based on the parameters ψ; and detecting causal effects in climate based on the parameters ψ.