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Sri Lanka is affected by extreme precipitation events every year, which cause floods, landslides and tremendous economic losses. We use the ERA5 reanalysis data set to understand the association of extreme precipitation events with 30 weather patterns, which were originally derived to represent the variability of the Indian climate during January–December 1979–2016. We find that weather patterns that are most common during the northeast monsoon (December–February) and second intermonsoon (October–November) seasons produce the highest number of extreme precipitation events. Furthermore, extreme precipitation events occurring during these two seasons are more persistent than those during the southwest monsoon (May–September) and first intermonsoon (March–April) seasons. We analyze the modulation of extreme precipitation events by the Madden‐Julian Oscillation, and find that their frequency is enhanced (suppressed) in phases 1–4 (5–8) for most weather patterns. Plain Language Summary: Extreme rainfall events affect Sri Lanka every year, causing floods, landslides and tremendous losses. Thus, it is important to identify weather patterns that are associated with these events. Furthermore, it is important to understand how the dominant modes of the tropical intraseasonal variability, such as the Madden‐Julian Oscillation, modulate their occurrence. In this study, we use the European Centre for Medium‐Range Weather Forecasts ERA5 reanalysis data set to understand the association between extreme precipitation events and a set of 30 weather patterns that were originally derived to understand the variability of the Indian climate. Our results suggest that weather patterns that are most common during winter and autumn seasons produce the highest number of extreme precipitation events in Sri Lanka, and these events are more persistent than those occurring during summer and spring seasons. The frequency of extreme precipitation events is enhanced when the Madden‐Julian Oscillation is active over the Indian Ocean. Key Points: Extreme precipitation in Sri Lanka occurs most frequently in weather patterns associated with the northeast and second intermonsoon seasonsIn weather patterns with the largest frequency of extreme precipitation events, extreme precipitation can persist for five consecutive daysThe frequency of extreme precipitation events is enhanced in phases 1–4 of the Madden‐Julian Oscillation [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the application of gridded precipitation datasets for applications in Sri Lanka and to identify suitable products that can be used for hydrological applications in data sparse regions. Here, nine global datasets were considered for this study, and after initial screening, four datasets, such as Integrated Multi-Satellite Retrievals for GPM (IMERG) early run calibrated, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), European Center for Medium range Weather Forecasting (ECMWF)—ReAnalysis (ERA5) and Indian Monsoon Data Assimilation and Reanalysis (IMDAA), were selected for detailed comparison. These data products were analysed at monthly, seasonal and annual time scale for whole Sri Lanka and also for three distinguished climate zones (wet, intermediate and dry) for the period 2001–2015. The datasets were further evaluated based on their ability to capture the extreme events, based on recent designated dry year and wet year. All four datasets represent the annual and monthly climatology of Sri Lanka well; however, discrepancies are seen at seasonal scale. CHIRPS had the best performance in the wet zone and ERA5 in dry and intermediate zones. Although all datasets distinctly identified the wet year and dry year with respect to the normal average, the interannual variability was explicitly identified by CHIRPS and ERA5. Therefore, for replicating the national and zonal climatology, both ERA5 (dry and intermediate zones) and CHIRPS (wet zone) are suitable. With the perspective of hydrological applications, both datasets can be used in development of hydrological models for applications such as flood forecasting, reservoir management and climate change impact assessment as an alternative to observed rainfall data in data scarce regions. [ABSTRACT FROM AUTHOR]