Abstract: As a highly migratory fish species, understanding the relationship between resource changes of albacore (Thunnus alalunga) and climate change is crucial for its sustainable management. Considering the impact of climate change on population dynamics, we incorporated the climate indexes, such as Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO) index, into the Just Another Bayesian Biomass Assessment (JABBA) surplus production model for albacore in the Indian Ocean (IO-ALB). Six climate-integrated assessment models were established, each assuming a different effect of climate variability on the intrinsic growth rate (r), carrying capacity (K), or and their combined effects on population dynamics. The results show that climate effects had a significant impact on the model fitting performance, especially the climate-integrated models considering the influence of IOD, which had a high fitting accuracy. Comparison of the assessment results from the six models indicates a relatively better stock state when the IOD-based model was applied and an overfished condition when the MJO-based model was incorporated. The study also reveals insignificant direct effects of climate factors on r but a negative effect of MJO on K. The study highlights the importance of considering climate effects in stock assessments of albacore in the Indian Ocean and, demonstrates that by incorporating environmental indexes, the model can better reflect the population dynamics, leading to more reliable assessment results and providing a scientific basis for future assessment of oceanic fish population resources and formulation of sustainable fishing strategies.