Rainfall intensity‐duration‐frequency (IDF) curves are commonly used for the design of water resources infrastructure. Numerous studies reported non‐stationarity in meteorological time series. Neglecting to incorporate non‐stationarities in hydrological models may lead to inaccurate results. The present work focuses on the development of a general methodology that copes with non‐stationarities that may exist in rainfall, by making the parameters of the IDF relationship dependent on the covariates of time and climate oscillations. In the recent literature, non‐stationary models are generally fit on data series of specific durations. In the approach proposed here, a single model with a separate functional relation with the return period and the rainfall duration is instead defined. This model has the advantage of being simpler and extending the effective sample size. Its parameters are estimated with the maximum composite likelihood method. Two sites in Ontario, Canada and one site in California, USA, exhibiting non‐stationary behaviours are used as case studies to illustrate the proposed method. For these case studies, the time and the climate indices Atlantic Multi‐decadal Oscillation (AMO) and Western Hemisphere Warm Pool (WHWP) for the stations in Canada, and the time and the climate indices Southern Oscillation Index (SOI) and Pacific Decadal Oscillation (PDO) for the stations in United States are used as covariates. The Gumbel and the generalized extreme value distributions are used as the time‐dependent functions in the numerator of the general IDF relationship. Results show that the non‐stationary framework for IDF modelling provides a better fit to the data than its stationary counterpart according to the Akaike information criterion. Results indicate also that the proposed generalized approach is more robust than the common approach especially for stations with short rainfall records (e.g., R² of 0.98 compared to 0.69 for duration of 30 min and a sample size of 27 years).

Cloud climatologies are of great importance in the study of cloud dynamics and interactions with land, oceans and atmosphere. This study analyzes the climatology of Total Cloud Cover (TCC) over the United Arab Emirates (UAE). Monthly mean TCC data between 1984 and 2009 from ground observations at six UAE airport stations and the satellite‐based (CLARA A2) product are utilized.The hourly ground observations are first filtered to match the overpass times of CLARA‐A2 satellites over the UAE. Comparison between the datasets reveals higher TCC reports by CLARA‐A2. These are attributed to several factors, such as the underestimation of TCC by human observers and the region's aerosol loading, surface albedo and emissivity, which impact the quality of remote sensing retrievals. Satellites utilized for CLARA‐A2 are known to have undergone changes in their numbers and orbits over the years, prompting an investigation of its homogeneity. Change points are found to occur in 1991 and 1998, related to increased satellite measurements and sensor changes, respectively. Further investigations of the homogeneity of means and variances before and after the change points are also conducted, resulting in confirmation of the inhomogeneity induced by artifacts.Monthly means of hourly ground observations are then used for analysis of TCC climatology over the six stations. Fujairah has the highest TCC on average, due to its proximity to the Hajar mountains and the Oman gulf. Most of the stations exhibit significant decreasing trends, in addition to a change point occurring in 1998. This is theorized to be related to the El Niño Southern Oscillation phase change occurring in the same period. The potential teleconnection is explored further through testing for correlations of TCC with the Oceanic Niño Index, with results showing a strong link in the fall–winter seasons.

A two-dimensional mathematical model of moist air convection in the sub-cloud and cloud layers is proposed. A theoretical analysis of the influence of near ground atmospheric parameters on the development of sub-cloud and cloud convection is provided, and the criteria of convection development are considered. As a rule, this relationship is parameterized in general circulation, regional or mesoscale models of the atmosphere. Therefore, achieving a more complete and correct understanding of this relationship can lead to an improvement in the accuracy of weather forecasts. The mathematical model describes the system of the equations of motion, heat conductivity and the continuity equations for a two-dimensional vertical plane. The approximate analytical solution of the system of equations is obtained. Expressions for the estimation of the convection height and height of maximum velocity are derived for vertical and horizontal components of updraft wind and for vertical distribution of temperature. From the expressions obtained, the criterion of sub-cloud convection development is derived. The expressions for the convection parameters at a condensation level have also been formulated, from which the criterion of cloud development through convection is derived. It is established that the development of cloud convection depends on absolute values of the dew point deficit in a near-surface layer and, in a greater degree, on vertical gradients of water vapor mass fraction. It is shown that at certain critical values of a vertical gradient of water vapor mass fraction “explosive convective growth” is observed. The application of the obtained results to artificial stimulation of convection by means of air heating in the near-ground atmosphere has shown that the success of such an application and the required air heating-up depend on: (i) the vertical temperature gradient; (ii) the vertical dew-point gradient; and (iii) the value of the dew point deficit in the near-ground layer. The analysis performed has shown the possibility of successful stimulation of artificial convection under specific favorable atmospheric conditions.

As climate impacts accelerate, ‘climate change hotspots’ (CCHs) are increasingly used to prioritise limited adaptation resources. However, hotspot conceptualisation remains fragmented. To address this gap, this study first conducts an evidence synthesis of 353 studies to investigate how CCHs are conceptually defined and analytically identified across four major climate hazards (heat, drought, flood and snow). This synthesis shows that most existing hotspot methodologies are dominated by potential hazard intensity, while socio-economic dimensions, stakeholder knowledge and impact-based validation are rarely integrated. To address this knowledge gap, we develop a bottom-up integrated framework for CCHs identification. Transitioning away from a top–down hazard-centric approach, our bottom-up approach integrates biophysical and socio-economic data with event-based storylines and stakeholder consultation. The transferability of our framework is demonstrated through two European case studies, snow avalanches in Alps and Carpathians, and flash flood in Trentino-Alto Adige, Italy. The case studies identify hotspots by intersecting climate hazard data with local geographic characteristics and socio-economic information. In both applications, historical event-based storylines ground quantitative risk classifications in real-world disaster dynamics, while stakeholder consultations refine the localised exposure. The results reveal that Alpine and Carpathian snow hotspots are projected to persist and intensify by 2100. The flash flood hotspots remain concentrated in alluvial valley bottoms, where the hazard converges with dense socio-economic and infrastructural exposure. Our integrated framework provides an adaptable and transferable approach for identifying CCHs. It can serve as good practice and can support the prioritisation of adaptation effort and planning across administrative regions and sectors.