Abstract Mexico ranks among the most disaster-prone countries worldwide, yet the differentiated effects of natural hazard-induced disasters on household expenditures remain insufficiently understood. This study examines the long-term impacts of diverse natural hazards—including geophysical, hydrological, meteorological, and climatological events—on key household spending categories critical to well-being, such as housing, education, health, and food, over the past two decades in Mexico. By addressing significant gaps in the literature, this research provides novel insights into how distinct types and severities of natural hazard-induced disasters reshape household decision-making and socio-economic outcomes in a highly vulnerable context. This study is the first to analyze how various types of natural hazards influence spending in critical areas such as education, housing, self-consumption and health, offering a unique perspective on the socioeconomic repercussions of natural hazard-induced disasters. At the core of this study lies a municipal-level panel dataset that integrates environmental disaster records with detailed household microdata, enabling a comprehensive spatial and temporal analysis of their impacts. The results reveal substantial heterogeneity in expenditure responses across categories, driven by hazard type and event severity, with important implications for household welfare. These findings underscore the critical need for targeted policy interventions to mitigate socioeconomic vulnerabilities and enhance adaptive capacity in the face of increasing environmental shocks associated with global change.

Abstract Drought is among the most severe hydro-meteorological events, and it is expected to become even more critical and recurrent in multiple regions worldwide. The availability of hydrological information months ahead is crucial for developing effective early warning strategies, which often remain focused on meteorological drought, and to support effective adaptation programs and enhanced preparedness. The Copernicus Emergency Management System (CEMS) provides seasonal ensemble forecasts of river discharge based on the LISFLOOD hydrological model simulations, which are driven by seasonal meteorological ensemble forecasts from the seasonal forecast system version 5 (SEAS5) by the European Centre for Medium-Range Weather Forecasts (ECMWF). In this study, we evaluate the performance of these forecasts in detecting hydrological drought in the period 1991–2022 using the ECMWF reanalysis ERA5 as a proxy for the observational reference. The main results show that the skill of the ensemble mean of the Standardized Streamflow Index (SSI) is high at both 1- and 3-month time horizons; lower-skill forecasts (but still informative) can be achieved up to 6 months ahead. The forecast always outperforms trivial ones done by using the latest available state of the reanalysis, with the highest added value, on average, in the Northern Hemisphere. Seasonality in average river discharge is highlighted as one of the main drivers of the differences in skill between seasons. Additionally, the dominant role of inter-annual variability in initial conditions (soil moisture) and meteorological forcings (precipitation) on forecast skill is observed in summer and spring–summer for the former, and in winter and autumn–winter for the latter. Besides the high skill observed with the ensemble mean, the signal-to-noise ratio (SNR) of the ensemble forecast is identified as key metric to operationally assess and communicate forecast reliability.