Abstract Robust projections of the Indian summer monsoon rainfall (ISMR) are critical as it provides 80% of the annual precipitation to more than 1 billion people who are very vulnerable to climate change. However, even over the historical period, state-of-the-art climate models have difficulties in reproducing the observed ISMR trends and are affected by a large intermodel spread, which questions the reliability of ISMR projections. Such uncertainty could come from internal variability or model biases. Here, we study the impact of the latter on the historical forced change of ISMR in 34 models from CMIP6. First, we show that models’ biases over India do not significantly impact how they simulate the historical change of ISMR. However, we do find statistically significant relationships between ISMR historical forced changes and remote rainfall and temperature biases within the tropics by using a maximum covariance analysis (MCA). Our results highlight the key role of tropical Pacific sea surface temperature (SST) mean state biases as an important source of intermodel spread in the ISMR change. The physical mechanisms underlying these statistical relationships between ISMR change and the intermodel spread of Pacific SST biases are finally explored. We found that models having El Niño/La Niña–like mean SST bias in the Pacific tend to exhibit El Niño/La Niña–like changes over the historical period, impacting ISMR through a shift in the Walker circulation and Rossby wave propagation across the Pacific.