This paper presents trends in downward surface shortwave radiation (SSR) over Europe, which are based on the 56 longest series available from the Global Energy Balance Archive that are mainly concentrated in central Europe. Special emphasis has been placed on both ensuring the temporal homogeneity and including the most recent years in the data set. We have generated, for the first time, composite time series for Europe covering the period 1939–2012, which have been studied by means of running trend analysis. The mean annual SSR series shows an increase from the late 1930s to the early 1950s (i.e., early brightening), followed by a reduction until mid‐1980s (i.e., global dimming) and a subsequent increase up to the early 2000s (i.e., global brightening). The series ends with a tendency of stabilization since the early 21st century, but the short time period is insufficient with regard to establishing whether a change in the trend is actually emerging over Europe. Seasonal and regional series are also presented, which highlight that similar variations are obtained for most of the seasons and regions across Europe. In fact, due to the strong spatial correlation in the SSR series, few series are enough to capture almost the same interannual and decadal variability as using a dense network of stations. Decadal variations of the SSR are expected to have an impact on the modulation of the temperatures and other processes over Europe linked with changes in the hydrological cycle, agriculture production, or natural ecosystems. For a better dissemination of the time series developed in this study, the data set is freely available for scientific purposes.

Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite rainfall products are exploited to study the spatial and temporal variability of East Africa (EA, 5S–20N, 28–52E) rainfall between 1983 and 2015. Time series of selected rainfall indices from the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices are computed at yearly and seasonal scales. Rainfall climatology and spatial patterns of variability are extracted via the analysis of the total rainfall amount (PRCPTOT), the simple daily intensity (SDII), the number of precipitating days (R1), the number of consecutive dry and wet days (CDD and CWD), and the number of very heavy precipitating days (R20). Our results show that the spatial patterns of such trends depend on the selected rainfall product, as much as on the geographic areas characterized by statistically significant trends for a specific rainfall index. Nevertheless, indications of rainfall trends were extracted especially at the seasonal scale. Increasing trends were identified for the October–November–December PRCPTOT, R1, and SDII indices over eastern EA, with the exception of Kenya. In March–April–May, rainfall is decreasing over a large part of EA, as demonstrated by negative trends of PRCPTOT, R1, CWD, and R20, even if a complete convergence of all satellite products is not achieved.

We analyse recent trends and variability of observed near‐surface wind speed from 19 stations across Saudi Arabia (SA) for 1978–2013. The raw wind speed data set was subject to a robust homogenization protocol, and the stations were then classified under three categories: (1) coast, (2) inland and (3) mountain stations. The results reveal a statistically significant (p < 0.05) reduction of wind speed of −0.058 m s⁻¹ dec⁻¹ at annual scale across SA, with decreases in winter (−0.100 m s⁻¹ dec⁻¹) and spring (−0.066 m s⁻¹ dec⁻¹) also detected, being non‐significant in summer and autumn. The coast, inland and mountain series showed similar magnitude and significance of the declining trends across all SA series, except for summer when a decoupled variability and opposite trends of wind speed between the coast and inland series (significant declines: −0.101 m s⁻¹ dec⁻¹ and −0.065 m s⁻¹ dec⁻¹, respectively) and the high‐elevation mountain series (significant increase: +0.041 m s⁻¹ dec⁻¹) were observed. Even though wind speed declines dominated across much of the country throughout the year, only a small number of stations showed statistically significant negative trends in summer and autumn. Most interestingly, a break in the stilling was observed in the last 12‐year (2002–2013) period (+0.057 m s⁻¹ dec⁻¹; not significant) compared to the significant slowdown detected in the previous 24‐year (1978–2001) period (−0.089 m s⁻¹ dec⁻¹). This break in the slowdown of winds, even followed by a non‐significant recovery trend, occurred in all seasons (and months) except for some winter months. Atmospheric circulation plays a key role in explaining the variability of winds, with the North Atlantic Oscillation positively affecting the annual wind speed, the Southern Oscillation displaying a significant negative relationship with winds in winter, spring and autumn, and the Eastern Atlantic negatively modulating winds in summer.

Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind‐related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be impacted by several artefacts, both temporally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of theR package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homogeneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short‐term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long‐term variability of extreme winds across Australia.

High quality and long-term precipitation data are required to study the variability and trends of rainfall and the impact of climate change. In developing countries like Morocco, the quality of climate data collected from various weather stations faces numerous obstacles. This paper presents methods for collecting, correcting, reconstructing, and homogenizing precipitation series of Morocco’s Fez-Meknes region from 1961 to 2019. Data collected from national specialized agencies based on 83 rain gauge stations was processed through an algorithm specially designed for the homogenization of climatic data (Climatol). We applied the Mann-Kendall test and Sen’s slope estimator to raw and homogenized data to calculate rainfall trend magnitudes and significance. The homogenization process allows for the detection of a larger number of stations with statistically significant negative trends with 95% and 90% confidence levels, particularly in the mountain ranges, that threatens the main sources of water in the largest watershed in the country. The regionalization of our rain gauge stations is highlighted and compared to previous studies. The monthly and annual means of raw and homogenized data show minor differences over the three main climate zones of the region.