Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.

Sandy shorelines respond to oceanographic and geologic processes at a variety of temporal (e.g., seconds to decades) and spatial (e.g., surf zone to ocean basin wide) scales. Thus, forecasting short-and long-term shoreline change remains difficult. Accurate predictions of shoreline movement in response to sea-level rise, changing wave climates, and reduced sediment supplies are increasingly sought to support coastal management, often out of fears that many beaches may be severely impacted by climate change (Le Cozan-

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Changes in the shoreline position are due to the interaction and feedbacks between a variety of processes affecting hydrodynamics and sediment transport. Each process is often characterized by a different dominant timescale so that the short-and long-term shoreline evolution remains difficult to predict. Cross-shore sediment transport is generally considered the main control of shoreline evolution at seasonal to interannual timescales mainly driven by changes in the wave height and period (Kriebel & Dean, 1985; Miller & Dean, 2004), and over much longer timescales due to sea level rise (SLR) (Bruun, 1962). Longshore processes on open coastlines typically become more relevant over intermediate and long timescales (decades to centuries) (Ashton et al., 2001; Hanson, 1989). Other processes related to sediment supply, tectonics, anthropogenic interventions (Le Cozannet et al., 2019; Ludka et al., 2018) may also be superimposed. On wave-dominated coastlines, bulk parameters (wave height, H s , wave period, T p , and/or wave direction θ) are used as drivers in numerical models to simulate shoreline change. Wave bulk parameters in turn depend on atmospheric patterns which can be captured by sea level pressure (SLP), fields, and gradients (Camus et al.

Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Using global satellite derived shoreline positions from 1993 to 2019 and a variety of reanalysis products, here we show that shorelines are under the influence of three main drivers: sea-level, ocean waves and river discharge. While sea level directly affects coastal mobility, waves affect both erosion/accretion and total water levels, and rivers affect coastal sediment budgets and salinity-induced water levels. By deriving a conceptual global model that accounts for the influence of dominant modes of climate variability on these drivers, we show that interannual shoreline changes are largely driven by different ENSO regimes and their complex inter-basin teleconnections. Our results provide a new framework for understanding and predicting climate-induced coastal hazards.