Understanding and predicting extreme weather events is essential for effective hazard prevention and risk management. However, achieving these objectives is challenging, as such events are often driven by nonlinear and/or multiscale processes, and involve multiple interactions within the climate system. In this thesis we employ complex network-based techniques and stochastic modeling to examine three large-scale weather and climate phenomena recognized for their association with extreme weather conditions: atmospheric blocking events, the El Niño–Southern Oscillation (ENSO), and the Madden–Julian Oscillation (MJO).