[PERSON] et al. [10] analyzed a problem of a population bias on tornado reporting in Europe. To account for this bias, a Bayesian modeling approach was used based on tornado observations and the population density for relatively small regions of Europe. The results indicated that the number of tornadoes could be 53% higher than are currently reported. The largest adjustments produced by the model pertain to Northern Europe and some Mediterranean regions. [PERSON] et al. [11] developed a machine learning framework for the assessment of a combined seismic and hydraulic risk at the regional scale. The machine learning techniques were used to aggregate large datasets made of many variables different in nature. The framework is applied to the case study of the Emilia Romagna region, for which the different municipalities are grouped into four homogeneous clusters ranked in terms of the relative levels of combined risk. The proposed approach proved to be robust and delivered a very useful tool for multi-hazard modeling at the regional scale. [PERSON] et al. [12] analyzed meteorological and hydrological drought risk at a regional scale in the Soan basin in Pakistan. The spatiotemporal analysis, statistical approaches, including regression analysis, trend analysis using Mann-Kendall, and moving average, were used to find a linkage between these drought types, the significance of the variations, and the lag time identification, respectively. The overall analysis indicated an increase in the frequency of both hydrological and meteorological droughs during the last three decades. [PERSON] et al. [13] systematically analyzed the National Flood Insurance Program (NFIP) claims hazard data in Florida. The claims with a presumably incorrect cause of loss fields were identified and revised by adding a variety of other available information. These datasets included tropical cyclone events, rainfall maxima, and distances to the nearest coast. The revised NFIP claims data will be intensively used to validate the outcomes from flood hazard (surge, wave, and inland flooding) models and to develop a flood vulnerability model in the forthcoming Florida Public Flood Loss Model (FPFLM). [PERSON] et al. [14] proposes a qualitative multi-hazard risk analysis methodology in the case of combined seismic and flood risk, using PROMETHEE, a multiple-criteria decision analysis technique. The present case study is a multi-hazard risk assessment of the Ferrara province (Italy). The proposed approach provides an original and flexible methodology to qualitatively prioritize the urban centers affected by multi-hazard risks at the regional scale. It delivers a useful tool to stakeholders involved in the processes of hazard management and disaster mitigation. [PERSON] et al. [15] analyzed a climatic multi-hazard risk for Greece, as the first-ever attempt to enhance scientific knowledge for the identification and definition of hazards, a critical element of risk-informed decision making. Many hazards (heatwaves, cold spells, torrential rainfall, snowstorms, and windstorms) were considered to correctly capture the country's susceptibility to climate extremes. The findings highlighted the areas that are exposed to multiple climate hazards in the country, considering the influence of the highly complex topography. All the contributions gathered in the present special issue contribute to the crucial societal challenge of reducing human and material losses induced by natural hazards caused by extreme climate changes and earthquakes. For this purpose, the continuous upgrade of comprehensive databases of individual risks and the development of new procedures and methodologies for hazard and risk assessment are key to the implementation of effective actions. The present studies show the usefulness of modern technologies, such as multi-criteria decision-making methods coupled with GIS, machine learning, and artificial intelligence.