Severe accident (SA) analysis of nuclear power plants relies on system codes such as MAAP, whose computational cost limits large-scale applications including dynamic probabilistic safety assessment and reinforcement learning. This study proposes the Accident Binary-Continuous Transformer (ABC-Transformer), a decoder-only surrogate combining causal self-attention with temporal attention pooling. It jointly embeds continuous thermal-hydraulic variables with binary safety-system and severe accident management guideline (SAMG) states, enabling 5-minute-resolution forecasting over 72-hour transients. Trained on 11,000 MAAP simulations of a total loss of component cooling water scenario for the OPR1000, it achieved lower one-step teacher-forcing error than CNN and recurrent baselines at fine resolutions. Autoregressive reliability was characterized as a full per-scenario distribution over 1100 held-out scenarios. For scheduled-sampling (SS) training, the median $R^2$ is $0.947 \pm 0.006$ and $3.8 \pm 1.0$% of scenarios fail ($R^2<0$; mean $\pm$ SD over four training seeds), while errors accumulate with rollout length. A Mahalanobis self-distance computed from each surrogate rollout, before comparison with the MAAP reference, ranks these failures (ROC-AUC 0.895, reference seed). A threshold at the 40th percentile of calibration scores accepts $36.8 \pm 0.5$% of 550 evaluation scenarios, with no accepted rollout below $R^2 = 0$ for any seed, and routes the rest to MAAP.

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