Assessment of local dynamics in extreme precipitation frequency using direct sequential cosimulation ANA CRISTINA COSTA Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Campus de Campolide, 1070-312 Lisboa PORTUGAL ccosta@isegi.unl.pt http://www.isegi.unl.pt AMILCAR SOARES Centro de Recursos Naturais e Ambiente Instituto Superior Técnico Av. Rovisco Pais, 1049-001 Lisboa PORTUGAL ncmrp@alfa.ist.utl.pt http://cerena.ist.utl.pt/ MARIA JOÃO PEREIRA Centro de Recursos Naturais e Ambiente Instituto Superior Técnico Av. Rovisco Pais, 1049-001 Lisboa PORTUGAL maria.pereira@alfa.ist.utl.pt http://cerena.ist.utl.pt/ RITA DURÃO Centro de Recursos Naturais e Ambiente Instituto Superior Técnico Av. Rovisco Pais, 1049-001 Lisboa PORTUGAL rmdurao@alfa.ist.utl.pt http://cerena.ist.utl.pt/ Abstract: This study evaluates space-time dynamics in extreme precipitation frequency by calculating a climate index at stations with records within the 1940–1999 period in the South of Portugal. This index is based on the annual count of days with precipitation above the 30 mm threshold (R30mm). Direct sequential cosimulation (coDSS) with elevation is used in the spatial interpolation and uncertainty assessment of the extreme precipitation index. The methodology incorporates space-time models that follow the premises that elevation and precipitation extremes may interact differently not only in space, but also through time. The results indicate that the relationship between elevation and the R30mm index has decreased through time over the study region. Moreover, the spatial patterns of precipitation extremes have become more homogenous during the last decades of the twentieth century. The more frequent rainfall events occur in the mountainous areas of the South (Algarve region). Accordingly, many areas of Algarve are at risk of water erosion and floods caused by extreme precipitation events. Regions where the distribution of precipitation extremes shows greater spatial variability, thus more uncertainty, correspond to regions less densely sampled. However, the uncertainty in mountainous regions is noticeably small given that elevation was used as secondary exhaustive information. Key-Words: Climate dynamics; geostatistics; space-time patterns; stochastic simulation; uncertainty; local trends ENVIRONMENTAL SCIENCE AND SUSTAINABILITY ISSN: 1790-5095 31 ISBN: 978-960-474-136-6