Drylands support over 2 billion people and are major providers of critical ecosystem goods and services across the globe. Drylands, however, are one of the most susceptible biomes to degradation. International programmes widely recognize dryland restoration as key to combating global dryland degradation and ensuring future global sustainability. While the need to restore drylands is widely recognized and large amounts of resources are allocated to these activities, rates of restoration success remain overwhelmingly low. Advances in understanding the ecology of dryland systems have not yielded proportional advances in our ability to restore these systems. To accelerate progress in dryland restoration, we argue for moving the field of restoration ecology beyond conceptual frameworks of ecosystem dynamics and towards quantitative, predictive systems models that capture the probabilistic nature of ecosystem response to management. To do this, we first provide an overview of conceptual dryland restoration frameworks. We then describe how quantitative systems framework can advance and improve conceptual restoration frameworks, resulting in a greater ability to forecast restoration outcomes and evaluate economic efficiency and decision-making. Lastly, using a case study from the western United States, we show how a systems approach can be integrated with and used to advance current conceptual frameworks of dryland restoration. Synthesis and applications. Systems models for restoration do not replace conceptual models but complement and extend these modelling approaches by enhancing our ability to solve restoration problems and forecast outcomes under changing conditions. Such forecasting of future outcomes is necessary to monetize restoration benefits and cost and to maximize economic benefit of limited restoration dollars. © 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society.