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• We developed a new function to model soil moisture driven grassland phenology. • Both growth and senescence rates changed nonlinearly with drying soil. • Incorporating the nonlinearity reduced model error and improved drought responses. • The model-data synthesis provides guidelines to improve grassland phenology modelling. Grassland responses to rainfall are characterised by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions of growth and senescence in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 7%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species' responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models. [ABSTRACT FROM AUTHOR]