Coastal communities throughout the world are exposed to numerous and increasing threats, such as coastal flooding and erosion, saltwater intrusion and wetland degradation. Here, we present the first global-scale analysis of the main drivers of coastal flooding due to large-scale oceanographic factors. Given the large dimensionality of the problem (e.g. spatiotemporal variability in flood magnitude and the relative influence of waves, tides and surge levels), we have performed a computer-based classification to identify geographical areas with homogeneous climates. Results show that 75% of coastal regions around the globe have the potential for very large flooding events with low probabilities (unbounded tails), 82% are tide-dominated, and almost 49% are highly susceptible to increases in flooding frequency due to sea-level rise.

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Extreme wave heights are climate‐related events. Therefore, special attention should be given to the large‐scale weather patterns responsible for wave generation in order to properly understand wave climate variability. We propose a classification of weather patterns to statistically downscale daily significant wave height maxima to a local area of interest. The time‐dependent statistical model obtained here is based on the convolution of the stationary extreme value model associated to each weather type. The interdaily dependence is treated by a climate‐related extremal index. The model's ability to reproduce different time scales (daily, seasonal, and interannual) is presented by means of its application to three locations in the North Atlantic: Mayo (Ireland), La Palma Island, and Coruña (Spain).

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical‐downscaling model‐based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long‐term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea‐state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large‐scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea‐state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time‐dependent, climate‐driven offshore and coastal sea‐state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.