Short-term drought forecasting remains challenging in Mediterranean regions because of strong interannual climate variability. This study develops and verifies an integrated framework for one- to three-month-ahead forecasting of meteorological drought in the Tangerois watershed (northwestern Morocco) using monthly TerraClimate precipitation and temperature data for 1981–2024 extracted from 17 meteorological stations that share four distinct precipitation series. Drought is quantified with the one-month Standardized Precipitation Temperature Index (SPTI), computed per calendar month with parameters of the 1981–2009 training period. A hybrid ARIMA–ANN forecast feeds three ESP-type ensembles of increasing complexity: an equal-weight ensemble (EEDP), a Gaussian-kernel weighted ensemble driven by an integrated climate predictor with an ARIMA-forecasted bandwidth (MWEDP), and a copula-based conditional ensemble that couples precipitation and temperature (CEDP). Forecasts for June–August, issued at the end of May with predictors available at that date, were verified at four representative sites over 2010–2024 against climatology and persistence, and model differences were tested with a block bootstrap over test years. EEDP and MWEDP were statistically indistinguishable from climatology (mean squared error skill score, MSESS = −0.005 and +0.002) and significantly better than persistence (MSESS = +0.24 and +0.25), whereas CEDP was less accurate than climatology (MSESS = −0.17). Differences between the three ensembles were not significant, consistent with a predictor screening that lacked field significance. A wet-season experiment (October–December) gave the same conclusions. All forecasts inherited a wet bias from the drier 2010–2024 period. At these lead times, the framework adds no forecast skill beyond climatology, which should serve as the benchmark for operational drought outlooks in this region.

Urban heat islands (UHIs) and regional climate change are interacting to increase the likelihood of heat waves in metropolitan areas. The size and layout of cities are determined mainly by spatial planning and urban transportation rules. Still, the combined effect of these two factors on the likelihood of heat waves is complicated and dependent on several variables. This study uses remote sensing (RS) technologies and spatial analytic tools to investigate the connection between urbanization and climate change in Greater Kenitra, Morocco. The results show that urban growth has exacerbated UHIs, raising surface temperatures and aggravating problems related to significant changes in land cover and usage in the study region. By 2020, bare soil is expected to make up about 55% of the land surface, down from 71% in 1984. In the meantime, vegetation cover increased to 18% by 2020 from an average of 12% between 1984 and 2003. In the same way, the built-up area increased from 6% in 1984 to 17% in 2020. This study offers fresh perspectives on the complex interplay between Morocco’s and North Africa’s urbanization and climate change. Urban planning and climate change adaptation techniques in Kenitra can benefit significantly from the observed effects of urban growth on temperature trends. These findings may also be applicable in other areas with similar geoenvironmental features.

Rapid urbanization combined with global climate change is intensifying the Surface Urban Heat Island (SUHI) effect worldwide, posing significant risks to human health, thermal comfort, and quality of life in cities. Characterized by notably higher temperatures in urban areas compared to their rural surroundings, the SUHI phenomenon is driven by factors such as increased built-up density and reduced vegetation cover. In this context, open-source remote sensing data, particularly from the Landsat satellite series, play a crucial role in studying surface urban heat islands. Available freely, Landsat’s multispectral and thermal imagery provides extensive spatial coverage and consistent temporal frequency, enabling long-term diachronic analyses. This study leverages a 40-year time series (1984–2024) of Landsat thermal data to map surface temperature variations in urban environments between Kenitra and Rabat cities, facilitating the identification of heat-excess zones linked to anthropogenic factors. Based on the results obtained, the LU/LC maps show that the study area is characterized by the notable growth of urbanization over the period 1984–2024, particularly in the dynamic poles of the region such as the city centers of Kénitra, Rabat, and Sale. This dynamic is highlighted by an increase from 1.8% to 3% in the total area of the region, accompanied by a remarkable decrease in agricultural land and bare soils. The evaluation of the Random Forest (RF) model’s performance also indicates that it successfully classified the data and predicted the LU/LC classes effectively, as confirmed by metric indices such as the Receiver Operating Characteristic curve and the Kappa index, which present very high average values exceeding 90%. Furthermore, the exploitation of the thermal bands of Landsat images provided relevant information on surface temperature variation. The SUHI maps show that the Rabat-Sale-Kenitra (RSK) region experienced a progressive increase in temperature over the study period, rising from 27 °C in 1984 to 44 °C in 2024. This value could increase further due to the continuous dynamics of urbanization. Together, these tools provide a robust framework for understanding the spatiotemporal dynamics of surface urban heat islands and support sustainable urban planning.