Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware -they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offline modellearning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence suffix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1.8 billion search queries, 2.6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.

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Weather and climate extremes are critical drivers for deadly and costly natural disasters (IFRC, 2020; UN-DRR, 2020). Understanding their changes and causes have been staying high on the agenda of climate science and risk management sectors (Chen, Moufouma-Okia, et al., 2018). There is rising awareness that the impact of spatially and/or temporally correlated events tends to be disproportionately larger than that of singular hazards as well as the sum of them (Zscheischler et al., 2018). A new paradigm that moves beyond isolated extremes is therefore strongly advocated (Leonard et al., 2014). Compound events were for first time defined in a special report from the Intergovernmental Panel on Climate Change (SREX, Field et al., 2012) as multiple extreme or nonextreme events occurring (1) simultaneously at the same place; (2) concurrently across multiple regions; or (3) in rapid sequence, in the same location. The definition was further refined (Leonard et al., 2014; Zscheischler et al., 2018), with a typology recently proposed (AghaKouchak et al., 2018; Zscheischler et al., 2020). As this field burgeons, a growing body of studies has explored statistical frameworks, observed and projected changes, and attribution of diverse compound events, such as drought-heatwave (Alizadeh et al., 2020; Hao et al., 2013), costal floods combining heavy precipitation, high water level and storm surge (Wahl et al., 2015), and concurrent heatwaves across global breadbaskets

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Reliable and informative climate projections are fundamental to climate-related risk assessments (King et al., 2015). To create a sense of certainty, the projection information is preferentially presented in the form of externally forced changes (the so-called "signal") along with a "likely range" characterizing un-