Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as “drizzle bias”, specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the drizzle bias in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (random forest or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly pronounced when considering higher temporal resolutions. Both methods are illustrated on data from three EURO-CORDEX climate models. The two approaches are trained during a calibration period, and they are applied for the selected evaluation period.

We update observed temperature trends in the Mediterranean and the Middle East–North Africa (MENA) region, assess their temporal and spatial features and investigate possible influences from urbanisation. Monthly mean temperature time-series of 370 stations are acquired from the CRUTEM4.6 and GSOD datasets and converted into suitable format for statistical analysis. The calculated annual and seasonal temperature linear trends for 1981–2020 indicate a strong warming with a MENA station average annual trend equal to $$0.36 ^\circ {\hbox {C}}$$0 . 36 ∘ C /decade and a faster warming rate for spring ( $$0.43 ^\circ {\hbox {C}}$$0 . 43 ∘ C /decade) and summer ( $$0.45 ^\circ {\hbox {C}}$$0 . 45 ∘ C /decade). These trends are correlated with longitude, revealing faster warming rates over the eastern Mediterranean and Middle East. The stations are characterised as rural or urban centre with the use of the Global Human Settlement Model (GHS-SMOD) spatial grid that accounts for population, city size and shape for the year 2000. The trend derived for the urban centre stations is an annually averaged $$0.1 ^\circ {\hbox {C}}$$0 . 1 ∘ C /decade greater than for the rural or all stations, and more prominent during summer and autumn. Hence a discernible, but small, urbanisation signature is revealed.

The urban heat island (UHI) effect denotes that temperatures in urban areas are often higher compared to the rural background. This human‐induced phenomenon can exacerbate heat stress and adverse health impacts, adding to ongoing global warming. To examine the intensity and variability of the UHI effect, we used 40 years () of observational data (daily maximum and minimum temperature) from the Global Summary of the Day (GSOD), comprising about 1000 stations of varying temporal extent, spanning the Middle East and North Africa (MENA). The MENA is characterised by hot and dry summers and warms faster than other inhabited regions worldwide. The challenge in using data with diverse spatial and temporal extents is accounting for heterogeneities between each station comprising an urban–rural pair. One has to allow for differences in the distance between the pairs and, elevation, spatial–temporal changes in urbanisation as well as the distance from the coast. A new method is proposed, based on flexible statistical methods (Generalized Additive Models or GAMs), to quantify the temporal trend in the UHI effect while allowing for the aforementioned characteristics using regression splines of appropriately defined variables. A composition of high‐resolution satellite geospatial information, related to urbanisation properties and population data was utilised from the Global Human Settlement Layer database (GHSL‐SMOD), to characterise the stations in terms of their urbanisation type. These data were also used to quantify temporal changes in the extent of urbanisation relative to the surrounding areas. Results indicate consistent upward trends of the UHI effect in the MENA (up to °C), particularly at night (daily minimum temperature) during all seasons.

The urban heat island (UHI) is a well-known effect where the temperature is higher in a city compared to a rural area, defined as the temperature difference between an urban and a rural location. This is a challenging phenomenon because it exacerbates the heat stress on human health in addition to the on-going global warming. It is therefore important to understand temporal changes in the UHI effect in a warming climate. To examine the UHI effect intensity and variability, we use 40 years (1980-2019) of observational data (daily maximum, minimum, and mean temperature) from the Global Summary of the Day (GSOD), consisting of about 1000 stations of varying temporal extend, spanning the Middle East and North Africa (MENA) region where a faster warming rate has occurred than the other regions globally. The challenge in using data with such spatial and temporal extend is the need to allow for heterogeneities between each of the two stations used to define an urban-rural pair. For instance, one has to allow for the distance between the pair, elevation differences, spatial changes in urbanization as well as the distance from the coast. Here a new method is proposed based on flexible statistical methods (Generalized Additive Models –GAMs) to quantify the temporal trend in the UHI effect while allowing for the aforementioned characteristics using regression splines of appropriately defined variables. A composition of high-resolution satellite geospatial information related to urbanisation properties and population data was utilized from the Global Human Settlement (GHSL-SMOD), in order to characterise the stations in terms of their urbanization type. This was also used to quantify changes in the extend of urbanisation in an area surrounding the rural stations. Results indicate a consistently upward trend of the UHI effect, particularly at night time (Tmin), across all four seasons.

Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as 'drizzle bias,' specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well-represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the "drizzle bias" in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (Random Forests or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly pronounced when considering higher temporal resolutions. Both methods are illustrated on data from three EURO-CORDEX climate models. The two approaches are trained during a calibration period and they are applied for the selected evaluation period.

The ability of a regional climate model (RCM) to represent the summer urban heat island (UHI) effect is investigated for the Eastern Mediterranean and the Middle East (EMME). The Weather Research and Forecasting (WRF) model is used with a bulk urban parameterisation to dynamically downscale the ERA‐Interim re‐analysis data at 16 and 4 km resolution for the period 2000–2002. A regional evaluation against the ERA5‐Land dataset indicates that the model overestimates maximum and underestimates minimum temperatures by 1°C–2°C for air and 5°C for land. However, simulated air and land temperatures compare well with observations for urban and rural locations of the Global Surface Summary of the Day (GSOD) dataset, with the 16 km output showing better skill than the 4 km, possibly due to the lack of model physics optimization in the highest resolution. A detailed analysis of the modelled summer climatology for the city of Cairo reveals a 3°C–5°C air and surface heat island at night‐time and a 5°C–10°C cool island at day‐time for land, corroborated by corresponding urban/rural surface energy fluxes. By averaging similar calculations from 11 cities in the EMME, we derive a larger night‐time UHI for the surface (5°C–6°C) than for the air (3°C–4°C), and for day‐time a very large surface cool island (12.5°C) and a small canopy heat island (1°C–1.5°C). The results indicate the suitability of the simple urban model treatment for multi‐city regional climate change assessments in the EMME.