South Asian countries have been experiencing frequent drought incidents recently, and due to this reason, many scientific studies have been carried out to explore drought in South Asia. In this context, we review scientific studies related to drought in South Asia. The study initially identifies the importance of drought-related studies and discusses drought types for South Asian regions. The representative examples of drought events, severity, frequency, and duration in South Asian countries are identified. The Standardized Precipitation Index (SPI) was mostly adopted in South Asian countries to quantify and monitor droughts. Nevertheless, the absence of drought quantification studies in Bhutan and the Maldives is of great concern. Future studies to generate a combined drought severity map for the South Asian region are required. Moreover, the drought prediction and projection in the regions is rarely studied. Furthermore, the teleconnection between drought and large-scale atmospheric circulations in the South Asia has not been discussed in detail in most of the scientific literature. Therefore, as a take-home message, there is an urgent need for scientific studies related to drought quantification for some regions in South Asia, prediction and projection of drought for an individual country (or as a region), and drought teleconnection to atmospheric circulation.

In order to increase the flood resilience of cities (i.e., the ability to cope with flood hazards), it is also crucial to make critical infrastructure functions resilient, since these are essential for urban society. Cities are complex systems with many actors of different disciplines and many interdependent critical infrastructure networks and functions. Common flood risk analysis techniques provide useful information but are not sufficient to obtain a complete overview of the effects of flooding and potential measures to increase flood resilience related to critical infrastructure networks. Therefore, a more comprehensive approach is needed which helps accessing knowledge of actors in a structured way. Fort Lauderdale, Florida, United States has suffered from flood impacts, especially from disruptions in critical infrastructure. This paper shows how shared insight among different sectors and stakeholders into critical infrastructure resilience and potential resilience-enhancing measures was obtained using input from these actors. It also provides a first quantitative indication of resilience, indicated by the potential disruption due to floods and the effect of measures on resilience. The paper contributes to the existing literature on resilience specifically by considering the duration of disruption, the inclusion of critical infrastructure disruption in flood impact analysis, and the step from resilience quantification to measures.

Chronic and acute coastal flood risks in Miami-Dade County are assessed over the range of sea-level rise (SLR) scenarios for the coming decades. The HAZUS-MH coastal flood hazard modeling and loss estimation tool are used to determine flood extent and depth and corresponding monetary losses to buildings associated with different sea water levels (SWLs). The frequency of SWLs is estimated using a nonstationary mixture normal-generalized Pareto distribution under current condition and future SLR scenarios. Also, the least adaptation level to cope with SLR-induced amplification of coastal flooding is assessed in terms of an increase in flood threshold. The results indicate that under current sea-level conditions, coastal flood risks are predominantly from exposure to acute extreme events. However, chronic risks from repetitive nonextreme flooding may exceed those from extreme floods under future SLR scenarios. Therefore, adaptation strategies may incorporate consideration about chronic flooding to avoid increasing cumulative losses under future SLR scenarios.

High-resolution climate projections are essential for regional risk assessment; however, Earth System Models (ESMs) operate at scales far too coarse for local impacts. This review examines how machine learning (ML) downscaling can bridge this divide and addresses a key knowledge gap: how to achieve reliable, physically consistent downscaling under future climate change. This article synthesizes ML downscaling developments from 2010 to 2025, spanning early statistical methods to modern deep learning (e.g., convolutional neural networks (CNNs), generative adversarial networks (GANs), diffusion models, and transformers). The analysis introduces a new taxonomy of model families and frames the discussion around the “performance paradox”—the tendency for models with excellent historical skill to falter under non-stationary climate shifts. Our analysis finds that convolutional approaches efficiently capture spatial structure but tend to smooth out extremes, whereas generative models better reproduce high-intensity events at the cost of greater complexity. The study also highlights emerging solutions like physics-informed models and improved uncertainty quantification to tackle persistent issues of physical consistency and trust. Finally, the synthesis outlines a practical roadmap for operational ML downscaling, emphasizing standardized evaluation, out-of-distribution stress tests, and hybrid physics–ML approaches to bolster confidence in future projections.