为显著提升建筑消防安全设计过程中数值模拟验证的计算效率，该文构建了一种引入历史状态条件约束的时序条件生成对抗网络（temporal conditional generative adversarial network, TC-GAN），基于由35种不同单层建筑布局与2种随机火源位置组合而成的70个火灾案例数据集，进行了模型的训练、优化及评估，实现了单层建筑火灾温度场演化的快速预测。结果表明：通过对比多步损失、计划采样和视频判别器3种TC-GAN模型的优化策略，筛选出了计划采样为相对最优策略；优化后的TC-GAN模型相较传统pix2pix模型预测性能得到提升，安全高度处预测温度场的平均归一化均方根误差降低39.3%，结构相似性指数提高24.6%，2项指标性能分别达到0.098 4和0.884 2，且温度空间分布与关键温度测点时间演化预测曲线吻合度也相对显著提升。该文研究结果有效实现了单层建筑火灾演化的快速预测，节省了数值模拟的时间与计算成本，为建筑消防设计辅助评估提供了一种新的技术路径。

Objective: Building fire safety design commonly relies on computational fluid dynamics simulations to obtain fire temperature fields. However, simulating the complete fire process in complex scenarios is time-consuming and cannot readily meet rapid assessment requirements. Existing methods for predicting fire temperature fields with conditional generative adversarial networks mostly rely on static mappings from building conditions to single-frame temperature fields, without fully accounting for the temporal dependencies between temperature fields at adjacent time steps as a fire develops. To address this issue, this study develops a temporal conditional generative adversarial network (TC-GAN), incorporating historical-state constraints into the pix2pix model. This study further optimizes the model to mitigate error accumulation in autoregressive prediction, thereby enabling rapid dynamic prediction of temperature-field evolution in single-story building fires. Methods: A fire dataset was constructed using Fire Dynamics Simulator. It comprised 35 single-story building layouts, each configured with two fire-source locations, resulting in a total of 70 fire scenarios. Given the building layout, fire-source location, and prediction time, TC-GAN introduces the temperature field at the previous time step as a historical-state input and, during inference, continuously generates temperature-field sequences in an autoregressive manner. To mitigate error accumulation, three optimization strategies, namely, multi-step loss, scheduled sampling, and a video discriminator, were compared. Model performance was comprehensively evaluated using the normalized root mean square error (NRMSE), the structural similarity index measure (SSIM), temperature-evolution curves at key monitoring points, and the intersection over union (IoU) and area error of high-temperature regions above 70 ℃. Results: The results showed that TC-GAN with only historical-state constraints was affected by autoregressive error accumulation and exhibited lower predictive performance than pix2pix, whereas the model with scheduled sampling achieved the best performance. On the test set, the model with scheduled sampling reduced average NRMSE by 39.3% and increased average SSIM by 24.6% compared with pix2pix. Temperature-field visualizations showed that this variant captured the temperature diffusion process in complex building spaces more consistently, reducing the local high-temperature accumulation, spatial-pattern fragmentation, and abnormal diffusion observed in the pix2pix predictions. The predicted curves at key monitoring points likewise showed closer agreement with the reference temperature trends. For regions with temperatures above 70 ℃, the model with scheduled sampling increased the average IoU from 0.357 4 to 0.561 1 and decreased the average absolute area error from 0.276 5 to 0.212 2, indicating improved predictive capability for capturing the location and extent of high-temperature regions. Conclusions: Historical-state constraints supply temporal evolution information for building-fire temperature-field prediction, but effective error-suppression strategies are required to mitigate autoregressive error accumulation. Under the conditions investigated in this study, TC-GAN combining scheduled sampling with historical-state constraints achieved the best overall predictive performance, outperforming the conventional pix2pix model in temperature-field accuracy, spatial structural consistency, temporal evolution at key monitoring points, and prediction of high-temperature regions. This method enables rapid prediction of temperature-field evolution in single-story building fires, reduces the time and computational costs of conventional numerical simulation, and provides a potential technical pathway for building fire safety design and rapid fire-risk assessment.