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Natural disasters such as droughts and floods originate as a consequence of excessive high or low precipitation amount and/or frequency. Due to the temporal persistence of the latter, the disasters tend to cluster in time. Because global ocean–atmosphere teleconnection patterns with (multi‐)decadal oscillations are tightly related with the precipitation variability, it is useful to analyse precipitation variability at the same timescale to understand any possible connection between them. In this study, decadal oscillations of daily extreme precipitation are investigated using quantile perturbation method for 65 stations in Turkey for the period 1955–2014. Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), and Western Mediterranean Oscillation teleconnection patterns are examined for their relation with the extreme precipitation variability. The analyses are conducted for four climatic seasons and seven subregions. According to the analysis based on single drivers, NAO is identified as the most effective driver of Turkish extreme precipitation variability in winter, especially over regions influenced by the Mediterranean climate, whereas AO has a similar effect. When the teleconnection patterns are investigated in pairs, the combination of the NAO and SOI results in the strongest influence on the winter extremes. Though obvious differences are not recorded between the results of linear and nonlinear methods for single driver analysis, the nonlinear method is superior for the multiple driver analysis.

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This study investigates the impact of the spatio-temporal accuracy of four different sea surface temperature (SST) datasets on the accuracy of the Weather Research and Forecasting (WRF)-Hydro system to simulate hydrological response during two catastrophic flood events over the Eastern Black Sea (EBS) and the Mediterranean (MED) regions of Turkey. Three time-variant and high spatial resolution external SST products (GHRSST, Medspiration and NCEP-SST) and one coarse-resolution and timeinvariant SST product (ERA5-and GFS-SST for EBS and MED regions, respectively) already embedded in the initial and the boundary conditions datasets of WRF model are used in deriving near-surface atmospheric variables through WRF. After the proper event-based calibration is performed to the WRF-Hydro system using hourly and daily streamflow data in both regions, uncoupled model simulations for independent SST events are conducted to assess the impact of SST-triggered precipitation on simulated extreme runoff. Some localized and temporal differences in the occurrence of the flood events with respect to observations depending on the SST representation are noticeable. SST products represented with higher cross-correlations (GHRSST and Medspiration) revealed significant improvement in flood hydrographs for both regions. The GHRSST dataset shows a substantial improvement in NSE ($70%), RMSE reduction up to 20%, and an increase in correlation from 0.3 to 0.8 with respect to the invariable SST (ERA5) in simulated runoffs over the EBS region.The use of both GHRSST and Medspiration SST data characterized with high spatiotemporal correlation resulted in runoff simulations exactly matching the observed runoff peak of 300 m 3 /s by reducing the overestimation seen in invariable SST (GFS) in the MED region. Improved precipitation simulation skills of the WRF model with the detailed SST representation show that the hydrographs of GHRSST and Medspiration simulations show better performance compared to the simulated hydrographs by observed precipitation.