The aim of this study was to investigate the effects of climate and location while using the multivariate model of malnutrition.The joint semiparametric model of stunting, wasting, and overweight was fitted to 2015 child Malawi demographic health survey data with 5149 records. The MDHS was a cross-sectional study. The smooth functions for the non-parametric terms were the regression splines and the effect of location was smoothed by the Markov random field (MRF).Rainfall had a positive effect on stunting (β = 0.076, P = 0.044) and overweight (β = 0.854, P = 0.039). Mean temperature (= 1.220, P = 0.031) and distance to water body (β = 0.009, P = 0.049) also had a positive effect on wasting. Increased length of rainy season was associated with reduced overweight (β = –0.163, P = 0.042). Location was not a significant predictor of all malnutrition indicators, although there was observable spatial variation regarding overweight and wasting. There was significant positive correlation between stunting and overweight (ρ = 0.234; 95% confidence interval, 0.135–0.324). The findings on socioeconomic determinants are consistent with the literature. Nutrition interventions may target hot spot areas that have shown increased risk for overweight and wasting. The strategies to minimize malnutrition should focus on consequences of climate change like high rainfall, length of season, and temperature.