Stationary and non-stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River Basin - Brazil A.C.F. Xavier 1* , A.P. Rudke 2 , T. Fujita 3 , G.C. Blain 1 , M.V.B. de Morais 4 , D.S. Almeida 5 , S.A.A. Rafee 6, 7 , L.D. Martins 2 , R.A.F. Souza 8 , E.D. Freitas 6 , J.A. Martins 2 1 Agronomic Institute, Campinas, São Paulo, Brazil. 2 Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil . 3 Federal University of Technology - Paraná, Londrina, Paraná, Brazil. 4 Catholic University of the Maule- Talca, Maule, Chile 5 State University of Maringá, Maringá, Paraná, Brazil. 6 University of São Paulo, São Paulo, São Paulo, Brazil. 7 Lund University, Lund, Sweden 8 Amazonas State University, Manaus, Amazonas, Brazil. Corresponding author: Ana Carolina Freitas Xavier (anacarolinaf.xavier@gmail.com) Abstract The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River Basin- Brazil. The non-parametric Mann Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analyzed based on three generalized extreme values (GEV) models: Model 1 (stationary); Model 2 (non-stationary for location parameter) and Model 3 (non-stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non-stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb) and October (Aw, Cfa, and Cfb). Approximately 90% of the 1112 stations presented no trend regarding the GEV parameters. The non-stationarity This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/joc.6265 This article is protected by copyright. All rights reserved. Accepted Article