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In the present work, the trend of extreme rainfall indices in the Macro-Metropolis of São Paulo (MMSP) was analyzed and correlated with largescale climatic oscillations. A cluster analysis divided a set of rain gauge stations into three homogeneous regions within MMSP, according to the annual cycle of rainfall. The entire MMSP presented an increase in the total annual rainfall, from 1940 to 2016, of 3 mm per year on average, according to Mann-Kendall test. However, there is evidence that the more urbanized areas have a greater increase in the frequency and magnitude of extreme events, while coastal and mountainous areas, and regions outside large urban areas, have increasing rainfall in a better-distributed way throughout the year. The evolution of extreme rainfall (95th percentile) is significantly correlated with climatic indices. In the center-north part of the MMSP, the combination of Pacific Decadal Oscillation (PDO) and Antarctic Oscillation (AAO) explains 45% of the P95th increase during the wet season. In turn, in southern MMSP, the Temperature of South Atlantic (TSA), the AAO, the El Niño South Oscillation (ENSO) and the Multidecadal Oscillation of the North Atlantic (AMO) better explain the increase in extreme rainfall (R2 = 0.47). However, the same is not observed during the dry season, in which the P95th variation was only negatively correlated with the AMO, undergoing a decrease from the ‘70s until the beginning of this century. The occurrence of rainy anomalous months proved to be more frequent and associated with climatic indices than dry months.

A rapid increase in atmospheric water vapor is a fundamental ingredient for many intense rainfall events. High-frequency precipitable water vapor (PWV) estimates from a Global Positioning System meteorological site (GPS) are evaluated here for intense rainfall events during the CHUVA Vale field campaign in Brazil (November–December 2011), in which precipitation events of differing intensities and spatial dimensions, as observed by an X-band radar, have been explored. A wavelet cross-correlation analysis shows that there are important spikes in the PWV that precede the more intense rainfall events on a timescale from 30 to 60 minutes. The correlation and lags between the GPS-PWV and rainfall events are evaluated, and a sharp increase in the GPS-PWV prior to the more intense events has been found and termed GPS-PWV "jumps". These jumps are associated with water vapor convergence and the continued formation of cloud condensate and precipitation particles. The GPS-PWV time-derivative histogram for the period of 60 minutes before the rainfall event reveals different distributions for higher intensity events. This feature could indicate the occurrence of severe precipitation and consequently has the potential for application in nowcasting activities.