Global warming has intensified extreme heat events, posing growing climate risks. As a climate-sensitive region, China has experienced compound daytime and nighttime extreme heat, threatening health, ecosystems, and socioeconomic development. Accurate projections of future extreme heat in China under different emission scenarios are therefore essential for climate risk assessment, adaptation planning, and regional sustainable development. This study integrates the SimmEX extreme temperature index dataset and NEX–GDDP–CMIP6 climate model data to investigate six daytime and nighttime extreme temperature indices (TXx, TNx, TX90p, TN90p, SU, and TR). A spatiotemporal evaluation framework is developed to optimize the Multi-Model Ensemble (MME), and the Combined Nonstationary Cumulative Distribution Function matching (CNCDFm) method is applied for bias correction. The main findings are as follows: (1) CMIP6 models can generally reproduce the spatiotemporal characteristics of extreme heat over China. Intensity-based indices (TXx, TNx) showed the highest skill (spatial correlations 0.85–0.95), followed by absolute-threshold indices (SU, TR), while percentile-based indices (TX90p, TN90p) exhibited larger uncertainties. (2) MME performance did not improve monotonically with increasing ensemble size. The spatiotemporal evaluation framework identified optimal models for different indices, and the corresponding optimal MME sizes balancing accuracy and robustness were identified as 7 for TXx, 4 for TNx, 5 for TX90p, 4 for TN90p, 4 for SU, and 4 for TR. (3) CNCDFm improved MME–observation agreement, with greater improvements for TXx, TNx, SU, and TR than for the percentile-based TX90p and TN90p. Extreme heat is projected to increase across China, with stronger increases under higher-emission scenarios and distinct regional patterns among different indices. By the late 21^st century, TXx and TNx are projected to rise by 6.7 °C and 7.3 °C under SSP5-8.5, compared with 2.4 °C and 3.1 °C under SSP1-2.6. Nighttime indices exhibit stronger increases than daytime indices, highlighting greater extreme heat risks under higher emissions.