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Silicon carbide (SiC) power MOSFETs are widely applied to critical infrastructure in modern energy systems. Thus, accurately predicting its remaining useful lifetime (RUL) in real-world applications has become crucial. State-of-the-art explored its RUL through a power cycling test mostly considering constant environmental conditions (e.g., fixed temperature and humidity), which has resulted in considerable RUL prediction errors in real-world applications. This study directly integrates environmental factors into the RUL modeling to address this issue. Specifically, the junction temperature (Tj), on-state voltage (Vds,on), on-state resistance (Rds,on), and case temperature (TC) have been explored in various environmental conditions to understand their tight correlations with the RUL in the real world. Time series statistics models Autoregressive (AR) and Autoregressive Integrated Moving Average (ARIMA) have been used to predict SiC MOSFET RUL to gain new insights systematically.