[1] Understanding how extremes are changing globally, regionally, and locally is an important first step for planning appropriate adaptation measures, as changes in extremes have major impacts. The Intergovernmental Panel on Climate Change's synthesis of global extremes was not able to say anything about western central Africa, as no analysis of the region was available nor was there an adequate internationally exchanged long-term daily data set available to use for analysis of extremes. This paper presents the first analysis of extremes in this climatically important region along with analysis of Guinea Conakry and Zimbabwe. As per many other parts of the world, the analysis shows a decrease in cold extremes and an increase in warm extremes. However, while the majority of the analyzed world has shown an increase in heavy precipitation over the last half century, central Africa showed a decrease. Furthermore, the companion analysis of Guinea Conakry and Zimbabwe showed no significant increases.

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from http://www.metoffice.gov.uk/hadobs/hadex3 and http://www.climdex.org.

Observational analyses of changing climate extremes over the West Africa region have been limited by the availability of long and high‐quality datasets. To help address this gap, a climate extremes indices workshop was held in the Gambia in December 2011 with participants from 14 West African countries. The resulting analysis utilized 15 annual indices derived from observed daily temperatures and 10 annual indices derived from observed daily precipitation. The analysis was conducted for 166 meteorological stations in 13 countries for 2 periods: 1960–2010 and 1981–2010. The analyses of trends in the annual mean temperature indices have identified statistically significant increases of 0.16 °C/decade and 0.28 °C/decade for mean annual maximum and mean annual minimum temperatures, respectively, averaged over all available land stations in the region during the last 50 years. The seasonal‐temperature‐related indices show significant patterns of warming in all seasons. The annual mean of daily minimum temperature has increased more than the annual mean of daily maximum temperature leading to a decreasing trend in the diurnal temperature range. Warm days and warm nights have become more frequent, and cold days and cold nights have become less frequent. The analyses of precipitation‐based indices indicate spatially non‐coherent changes throughout the study area with few statistically significant trends over the longer period. Exceptions to this are the simple daily intensity index and maximum 5‐day precipitation, which show significant increasing regional trends over both the shorter and longer periods. Additionally, over the recent period (1981–2010) most of the precipitation related indices show significant trends towards wetter conditions. However, this period of increased rainfall follows a decade of significantly drier conditions in the region – it is not clear whether the recent upward trends reflect the ‘recovery’ from this long drought period or represents a long‐term response to warming.

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction.