A fish’s body condition is described by its weight given its length and is often positively associated with fitness. Atlantic cod (Gadus morhua) in the south-eastern Baltic Sea has experienced a drastic deterioration of its physiological status since the early 1990s to levels that compromise the growth or even survival of the population. Several hypotheses have been proposed (e.g., competition, hypoxia, lack of prey) and evaluated temporally using averages over large spatial scales (basin or population level). However, as these variables operate at local spatial scales and are heterogenous in space, it is important to evaluate the link between body condition and covariates on local scales as well. By applying a geostatistical model that includes spatially and spatiotemporally correlated random effects using Gaussian Markov random fields, we analyze the body condition of cod in the autumn (main feeding season) in relation to biotic and abiotic covariates at different spatial scales and their spatiotemporal dynamics. We find that body condition declined over the whole domain until 2008, after which a plateau was reached. Oxygen, sprat biomass (at the subdivision level), haul-level temperature, and the biomass of the benthic isopod Saduria entomon (to a lesser extent) where positively related to condition, whereas the haul-level density of cod and depth were negatively associated with condition. However, the effect sizes of these variables were small, such that even though cod are now found in deeper and less-oxygenated waters, this could not alone explain the steep decline that occurred between 1993–2008. In fact, latent spatial and spatiotemporal variation was several times larger in magnitude than any single covariate’s coefficient, and spatial and spatiotemporal random effects explained 6.5 times more variation than all fixed effects. However, body condition is a complicated trait to analyze as it builds up over a long time period. Hence, observational analysis of condition data should be complemented with e.g., tagging studies or otoliths analyses, before mechanistic links between condition and covariates can be determined. Understanding the drivers of spatiotemporal variation in body condition is critical for identifying impacts of environmental change and for the management of marine fish and fisheries.