## 1 Introduction  
The era of information technology and advances in high-performance computing are opening up possibilities to realistically simulate the evolution of the Earth system at scales not thought possible before. In hydrology, this has been labeled hyper-resolution, referring to a finer spatial discretisation than what was previously attempted at global scale [1; 2; 3] and it has triggered a fruitful debate on the importance of information-driven advances [4; 5]. The debate is partly about the resolution and model complexity needed to resolve and represent regional-scale water-cycle processes [6; 7; 8]. In weather and climate studies, calls for higher resolution have also been made [9; 10], as currently unresolved cloud processes are one of the obstacles for improved climate predictions. Attaining these finely resolved scales with the entire Earth System including the land, atmosphere, oceans and ice surfaces involves major technological advances in scalability [11] due to computing energy barriers, time-to-solution requirements, and hardware/software limitations. In this review, we emphasize the essential role of Earth Observations data from polar orbiting and geostationary satellites as a key driver for advancing models, and we present a number of examples of how satellite data combined with in situ observations have successfully driven model improvement. A global kilometric representation of the Earth surface that can feed into the next generation of weather and climate models will have to be driven by satellite observations [12; 13]. The expected increased use of subseasonal-to-seasonal (S2S) forecasts [14] makes it even more important to improve the representation of the land and ocean surface in current models, given the essential role of land-ocean-atmosphere interactions in S2S predictability. Such an ambitious goal can be achieved only via the synergistic development of models and model-data fusion techniques that make use of the wealth of satellite observations, ingested into the modelling chain, to genuinely characterize the details of land physiography, coastal and inland water and all surface heterogeneity that impacts water and energy fluxes. The drive to achieve physical realism in Earth surface modellingis common to hydrological and environmental applications [15] and resolution and model complexity are key ingredients. Not all surface variables can be observed routinely and constraining most sub-surface properties remains challenging. Therefore, the representation of uncertainties is essential in forecasting applications [16]. Information on the parameters necessary to describe the thermal, radiative, and hydrological properties of the surface must also be extracted, and uncertainties evaluated, so that the models can predict the main reservoirs and fluxes for water, energy and mass exchanges. The availability of stored energy and water within the Earth surface is further modulated by land use (such as irrigation, agricultural practice, forest management) and sub-surface properties which drive the high spatial heterogeneity. That is why there is a growing realization that human activities, impacts and feedback need to be represented in models [17]. The projected increase in the occurrence of extreme weather events (heatwaves, but also thoughts and floods in some regions [18]) in connection with global warming in the 21 st century, combined with the proven fact of observed warming trends since pre-industrial time [19], are both of global-scale and local relevance. The latest IPCC (Intergovernmental Panel on Climate Change) assessment [18] indicates that further warming, even for only 0.5 \({}^{\circ}\)C of global warming, would further increase the occurrence or intensity of warm spells on global scale, of heavy precipitation in several regions, and of droughs in some regions; heavy precipitation associated with tropical cyclones is also projected to increase with increased global warming.