This study examined the temporal and spatial patterns of diarrhoea in relation to hydro-meteorological factors in the Mekong Delta area in Vietnam. A time-series design was applied to examine the temporal pattern of the climate–diarrhoea relationship using Poisson regression models. Spatial analysis was applied to examine the spatial clusters of diarrhoea using Global Moran's I and local indicators of spatial autocorrelation (LISA). The temporal pattern showed that the highest peak of diarrhoea was from weeks 30–42 corresponding to August–October annually. A 1 cm increase in river water level at a lag of 1 week was associated with a small [0·07%, 95% confidence interval (CI) 0·01–0·1] increase in the diarrhoeal rate. A 1 °C increase in temperature at lag of 2 and 4 weeks was associated with a 1·5% (95% CI 0·3−2·7) and 1·1% (95% CI 0·1−2·3) increase in diarrhoeal risk, respectively. Relative humidity and diarrhoeal risk were in nonlinear relationship. The spatial analysis showed significant clustering of diarrhoea, and the LISA map shows three multi-centred diarrhoeal clusters and three single-centred clusters in the research location. The findings suggest that climatic conditions projected to be associated with climate change have important implication for human health impact in the Mekong Delta region.