The climate in Svalbard is undergoing amplified change compared to the global mean. This has major implications for runoff from glaciers and seasonal snow on land. We use a coupled energy balance–subsurface model, forced with downscaled regional climate model fields, and apply it to both glacier-covered and land areas in Svalbard. This generates a long-term (1957–2018) distributed dataset of climatic mass balance (CMB) for the glaciers, snow conditions, and runoff with a 1 km×1 km spatial and 3-hourly temporal resolution. Observational data including stake measurements, automatic weather station data, and subsurface data across Svalbard are used for model calibration and validation. We find a weakly positive mean net CMB (+0.09 m w.e. a⁻¹) over the simulation period, which only fractionally compensates for mass loss through calving. Pronounced warming and a small precipitation increase lead to a spatial-mean negative net CMB trend (−0.06 m w.e. a⁻¹ decade⁻¹), and an increase in the equilibrium line altitude (ELA) by 17 m decade⁻¹, with the largest changes in southern and central Svalbard. The retreating ELA in turn causes firn air volume to decrease by 4 % decade⁻¹, which in combination with winter warming induces a substantial reduction of refreezing in both glacier-covered and land areas (average −4 % decade⁻¹). A combination of increased melt and reduced refreezing causes glacier runoff (average 34.3 Gt a⁻¹) to double over the simulation period, while discharge from land (average 10.6 Gt a⁻¹) remains nearly unchanged. As a result, the relative contribution of land runoff to total runoff drops from 30 % to 20 % during 1957–2018. Seasonal snow on land and in glacier ablation zones is found to arrive later in autumn (+1.4 d decade⁻¹), while no significant changes occurred on the date of snow disappearance in spring–summer. Altogether, the output of the simulation provides an extensive dataset that may be of use in a wide range of applications ranging from runoff modelling to ecosystem studies.