Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources.

Given the critical role that snow plays in modulating current climate, it is vital to examine the contribution of the projected snow decline to the future warming. This is investigated over North America using regional climate modeling experiments with prescribed current and future snow conditions. Results indicate that the decline in snow contributes to future warming by up to 2°C and 4°C, respectively, to annual and spring temperature increases. Results also show that decline in snow depth and cover lead to the attenuation (amplification) of cold (warm) extremes. The rate of intensification of warm extremes is higher over the snow‐abundant regions of northern Canada—where current snow cover fraction is close to one. The largest attenuation in cold extremes, on the other hand, is over snow marginal regions of the Great Plains and Prairies. These results demonstrate the spatiotemporal differences in projected changes exclusively associated with snow cover and depth decline.

Inadequate representation and parameterization of sub-grid scale features and processes are one of the main sources for uncertainties in regional climate change projections, particularly for the Arctic regions where the climate change signal is amplified. Increasing model resolution to a couple of kilometers will be helpful in resolving some of these challenges, for example to better simulate convection and refined land heterogeneity and thus land–atmosphere interactions. A set of multi-year simulations has been carried out for the Canadian Arctic domain at 12 km and 3 km resolutions using limited-area version of the global environmental multi-scale (GEM) model. The model is integrated for five years driven by the fifth generation of the European Centre for medium-range weather forecast reanalysis (ERA-5) at the lateral boundaries. The aim of this study is to investigate the role of horizontal model resolution on the simulated surface climate variables. Results indicate that although some aspects of the seasonal mean values are deteriorated at times, substantial improvements are noted in the higher resolution simulation. The representation of extreme precipitation events during summer and the simulation of winter temperature are better captured in the convection-permitting simulation. Moreover, the observed temperature–extreme precipitation scaling is realistically reproduced by the higher resolution simulation. These results advocate for the use of convective-permitting resolution models for simulating future climate projections over the Arctic to support climate impact assessment studies such as those related to engineering applications and where high spatial and temporal resolution are beneficial.

Understanding snow–precipitation coupling mechanisms is of great importance both from theoretical and operational considerations. Here, carefully designed climate model experiments, with and without interactive snow, are conducted to study snow–precipitation coupling mechanisms over North America. Coupling hotspots are identified over southern Canada during December and over the central United States during January. The hotspot over southern Canada involves a positive snow–atmosphere feedback mechanism, whereby snow modifies the large‐scale atmospheric features, which resembles the positive phase of North Atlantic Oscillation. This favors storm activity and enhanced snow over the region. The coupling over the central United States during January, on the other hand, is tied to the albedo effect of snow, which leads to cooling of the lower atmosphere, which in turn determines the precipitation phase, favoring snow formation over rain. The results from this study, in general, are informative for sub‐seasonal to seasonal prediction of winter precipitation for the studied regions.

Accurate and skillful subseasonal forecasts have tremendous potential for sectors that are sensitive to hazardous weather and climate events. Analysis of prediction skill for snow water equivalent (SWE) and near-surface air temperature (T2m) is carried out for three (GEPS, GEFS, and FIM) global models from the subseasonal experiment (SubX) project for the 2000–14 period. The prediction skill of SWE is higher than the skill of T2m at week-3 and week-4 lead times in all models. The GEPS forecast tends to yield higher (lower) prediction skill of SWE (T2m) compared to the other two systems in terms of correlation skill score. The snow–temperature relationship in reanalysis is characterized by a strong negative correlation over most of the midlatitude regions and a weak positive correlation over high-latitude Arctic regions. All forecast systems reproduced well these observed features; however, the snow–temperature relationship is slightly weaker in the GEPS model. Despite the apparent lack of skill in temperature forecasts at week 4, all three models are able to predict the sign of temperature anomalies associated with extreme SWE conditions albeit with reduced intensity. The strength of the predicted temperature anomaly associated with extreme snow conditions is slightly weaker in the GEPS forecast compared to reanalysis and the other two models, despite having better skill in predicting SWE. These apparent disparities suggest that weak snow–temperature coupling strength in the model is one of the contributing factors for the lower temperature skill.

Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spatiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and K_Means clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient $$r=0.95$$r = 0.95 , $$rmsd=0.04{\mathrm{m}}^{3}{\mathrm{m}}^{-3}$$r m s d = 0.04 m 3 m - 3 with observations). Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered. In contrast, the western part of the country consistently depicted a wetter condition in all seasons. During summer (JJAS), the soil moisture variability is characterized by a strong east–west spatial contrast. The highest and lowest soil moisture values were observed across the country’s central western and eastern parts, respectively. Furthermore, analyses indicate that interannual variability of SM is dictated substantially by RF, though the impact on some regions is weaker. It is also found that ET likely drives the SM in the eastern part of Ethiopia due to a higher atmospheric moisture demand that ultimately invokes changes in surface humidity and rainfall. A composite analysis based on the extreme five wettest and driest SM years revealed a similar spatial distribution of wet SM with positive anomalies of RF across the country and ET over the southern regions. Remote SSTs are also found to have a significant influence on SM distribution. In particular, equatorial central Pacific and western Indian oceans SST anomalies are predominant factors for spatiotemporal SM variations over the country. Major global oceanic indices: Oceanic Nino Index (ONI), Indian Ocean Dipole (IOD), Pacific warm pool (PACWARMPOOL), and Pacific Decadal Oscillations (PDO) are found to be closely associated with the SM anomalies in various parts of the country. The associationship between these remote SST anomalies and local soil moisture is via large-scale atmospheric circulations that are linked to regional factors such as precipitation and temperature anomalies.