The purpose of this study is to document the codevelopmental trajectories of nighttime sleep duration and depression in Chinese middle-aged and older people and investigate the predictive role of green space in these trajectories.In a protective cohort study of 7422 middle-aged and elderly Chinese adults with surveys in 2011, 2013, 2015, 2018, and 2020, codevelopmental trajectories class(es) of nighttime sleep duration and depression were identified using parallel process latent class growth models. The relationships between the growth factors of two variables within each class were estimated using parallel process latent growth curve model. Multinomial logistic regression assessed the association of greenspace, measured using the Normalized Difference Vegetation Index, with codevelopmental trajectory class membership.Three codevelopmental trajectories classes were detected: parallel low sleep and high depression (10.9%); divergent decreasing sleep and increasing depression (12.4%); and parallel stable sleep and low depression (76.7%). Parallel process latent growth curve model revealed that the association between nighttime sleep duration and depression (i.e., intercepts and slopes) varies among different trajectories. Multinomial logistic regression analyses revealed that exposure to higher levels of green space would be associated with the trajectory class exhibiting parallel stable sleep and low depression.The codevelopment trajectories of sleep duration and depression exhibit distinct subgroups and the interrelationships vary across different subgroups. This information can inform prevention and intervention strategies, as each trajectory may represent heterogeneity among middle-aged and older adults. Higher green space was associated with lower likelihood of adverse codevelopmental trajectories of sleep and depression.Copyright © 2025. Published by Elsevier Inc.