The rainfall regime of the South of Portugal is Mediterranean with Atlantic influence. Long‐term series of reliable precipitation records are essential for land and water resources management, climate‐change monitoring, modelling of erosion and run‐off, among other applications for ecosystem and hydrological impact modelling. This study provides a qualitative classification of 106 daily rainfall series from stations located in the South of Portugal and evaluates temporal patterns in extreme precipitation by calculating a number of indicators at stations with homogeneous data within the 1955/1999 period. The methodology includes both absolute and relative approaches and a new homogeneity testing procedure, besides the application of other statistical tests. The proposed technique is an extension of the Ellipse test that takes into account the contemporaneous relationship between several candidate series from the same climatic area (SUR + Ellipse test). The results indicate that this technique is a valuable tool for the detection of non‐climatic irregularities in climate time series if the station network is dense enough. The existence of trends and other temporal patterns in extreme precipitation indices was investigated and uncertainty about rainfall patterns evolution was assessed. Three indices describing wet events and another three indicators characterizing dry conditions were analysed through regression models and smoothing techniques. The simple aridity intensity index (AII) reflects increases in the magnitude of dryness. Especially pronounced trends are found over most of southern Portugal in the 1955/1999 period, highlighting the fact that large areas are threatened by drought and desertification. The trend signals of the wetness indices are not significant at the majority of stations, but there is evidence of increasing short‐term precipitation intensity over the region during the last three decades of the twentieth century. Finally, the results also indicate that extreme precipitation variability and climate uncertainty are greater in recent times. Copyright © 2008 Royal Meteorological Society

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

Urban systems are not only major drivers of climate change but also the impact hotspots. In the context of the planet currently undergoing a process of greenhouse warming, and simultaneously predominantly urban based ever continuing population growth, our agglomerations became vulnerable to chain reactions triggered by climate related hazards. Hence, the reliable and cost-effective assessment of future impact is of high importance. While the climate community has invested significant effort to provide downscaling techniques yielding localized information on future climate extreme behaviours, these methods do not remain widely exploited in the process of urban planning. In this work, we discuss the underlying reasons and main challenges of the applicability of downscaling procedures in the real process of urban planning. This paper attempts to help bridge the gap between the communities of urban planners and climatology. In the beginning, we summarize the rationale for such cooperation, supporting the argument that the scale represents an important linkage between urbanistic and climate science in the process of designing an urban space. Secondly, we introduce the main families of downscaling techniques and their application on climate projections, also providing the references to profound studies in the field. Thirdly, special attention is given to previous works focused on the utilization of downscaled ensembles of climate simulations in urban agglomerations. Finally, we identify three major challenges of the wider utilization of climate projections and downscaling techniques, namely: (i) the scale mismatch between data needs and data availability, (ii) the terminology, and (iii) the IT bottleneck. The practical implications of these issues are discussed in the context of urban studies.