This study aimed to assess the impacts of climate change on streamflow characteristics of five tropical catchments located in Costa Rica. An ensemble of five General Circulation Models (GCMs), namely HadGEM2-ES, CanESM2, EC-EARTH, MIROC5, MPI-ESM-LR dynamically downscaled by two Regional Climate Models (RCMs), specifically HadRM3P and RCA4, was selected to provide an overview of the impacts of different climate change scenarios under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the 1961–1990 baseline period. The GR2M hydrological model was used to reproduce the historical monthly surface runoff patterns of each catchment. Following calibration and validation of the GRM2 model, the projected impact of climate change on streamflow was simulated for a near-future (2011–2040), mid-future (2041–2070) and far-future (2071–2100) for each catchment using the bias-corrected GCM-RCM multimodel ensemble-mean (MEM). Results anticipate wetter conditions for all catchments in the near-future and mid-future periods under RCPs 2.6 and 4.5, whereas dryer conditions are expected for the far-future period under RCP 8.5. Projected temperature trends indicate consistently warmer conditions with increasing radiative forcing and future periods. Streamflow changes across all catchments however are dominated by variations in projected precipitation. Wetter conditions for the near-future and mid-future horizons under RCPs 2.6 and 4.5 would result in higher runoff volumes, particularly during the late wet season (LWS). Conversely, dryer conditions for the far-future period under RCP8.5 would result in considerably lower runoff volumes during the early wet season (EWS) and the Mid-Summer Drought (MSD). In consequence, projected seasonal changes on streamflow across all catchments may result in more frequent flooding, droughts, and water supply shortage compared to historical hydrological regimes.

This work aims to examine the effect of bias correction (BC) methods on the development of Intensity–Duration–Frequency (IDF) curves under climate change at multiple temporal scales. Daily outputs from a 9-member CORDEX-CA GCM-RCM multi-model ensemble (MME) under RCP 8.5 were used to represent future precipitation. Two stationary BC methods, empirical quantile mapping (EQM) and gamma-pareto quantile mapping (GPM), along with three non-stationary BC methods, detrended quantile mapping (DQM), quantile delta mapping (QDM), and robust quantile mapping (RQM), were selected to adjust daily biases between MME members and observations from the SJO weather station located in Costa Rica. The equidistant quantile-matching (EDQM) temporal disaggregation method was applied to obtain future sub-daily annual maximum precipitation series (AMPs) based on daily projections from the bias-corrected ensemble members. Both historical and future IDF curves were developed based on 5 min temporal resolution AMP series using the Generalized Extreme Value (GEV) distribution. The results indicate that projected future precipitation intensities (2020–2100) vary significantly from historical IDF curves (1970–2020), depending on individual GCM-RCMs, BC methods, durations, and return periods. Regardless of stationarity, the ensemble spread increases steadily with the return period, as uncertainties are further amplified with increasing return periods. Stationary BC methods show a wide variety of trends depending on individual GCM-RCM models, many of which are unrealistic and physically improbable. In contrast, non-stationary BC methods generally show a tendency towards higher precipitation intensities as the return period increases for individual GCM-RCMs, despite differences in the magnitude of changes. Precipitation intensities based on ensemble means are found to increase with the change factor (CF), ranging between 2 and 25% depending on the temporal scale, return period, and non-stationary BC method, with moderately smaller increases for short-durations and long-durations, and slightly higher for mid-durations. In summary, it can be concluded that stationary BC methods underperform compared to non-stationary BC methods. DQM and RQM are the most suitable BC methods for generating future IDF curves, recommending the use of ensemble means over ensemble medians or individual GCM-RCM outcomes.