Abstract. General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Apart from the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- and large-scale variables. This study compares four statistical downscaling methods of bias correction (BC), the change factor of mean (CFM), quantile perturbation (QP) and an event-based weather generator (WG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000 for the weather station of Uccle located in Belgium. A set of drought-related aspects is analysed, i.e. dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 28-member ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced by four future scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability in the climate. A performance comparison of the downscaling methods reveals that the QP method outperforms the others in reproducing the magnitude and monthly pattern of the observed indicators. While all methods show a good agreement on downscaling total precipitation, their results differ quite largely for the frequency and length of dry spells. Using the downscaling methods, dry day frequency is projected to increase significantly in the summer months, with a relative change of up to 19 % for SSP5–8.5. At the same time, total precipitation is projected to decrease significantly by up to 33 % in these months. Total precipitation also significantly increases in winter, as it is driven by a significant intensification of extreme precipitation rather than a dry day frequency change. Lastly, extreme dry spells are projected to increase in length by up to 9 %.

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<p>Quito, the capital of Ecuador, is an Andean city experiencing two water challenges: urban flooding driven by extreme precipitation events and water scarcity in the dry season. Climate change is expected to increase the probability of the occurrence of flash floods, sewer overflows, and landslides because of more intense precipitation. It might moreover reduce the river discharge in the dry season due to an increase in temperature and evapotranspiration. The previous studies in the region have used a limited number of climate models and have not focused on short-duration events, presenting biased impacts of climate change. <br />To address this knowledge gap, this research employs an ensemble of 19 state-of-the-art CMIP6 general circulation models (GCMs) to analyze climate change impacts on intensity- duration-frequency (IDF) curves, urban flooding, and river discharge in Quito under four plausible future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Daily precipitation and temperature simulations are spatially downscaled with the delta change and quantile perturbation approaches. Temporal downscaling is then applied to obtain sub-daily precipitation time series and statistics in the form of IDF curves. The uncertainty contribution of the extreme value analysis is considered by including five statistical distributions for IDF estimations. Furthermore, composite design storms are built based on historical hyetographs recorded in the city and then applied to a calibrated hydraulic model (SWMM) for a part of Quito´s combined sewer system. Climate change impacts on the urban area are expressed as changes in the IDF curves and the flood volume. <br />To analyze climate change impacts on river discharge, three conceptual hydrological models (NAM, GR4J, and VHM) are calibrated in one of the water-supplying catchments of the city, the San Pedro River. Here, the impacts are expressed as changes in the peak, mean, and low river discharges. The uncertainty contribution of the different components (climate models, emissions scenarios, hydrological models, and extreme value distributions) is quantified by a variance decomposition method. <br />The findings suggest an increase in the intensity of short-duration extreme precipitation events by 10-30% for the near future (2021-2050) and by 20-50% for the far future (2070- 2099). As a result of this intensification, the flood volume in the sewer network of Quito magnifies at critical points. Moreover, in the San Pedro River, the peak discharges are projected to increase by 5-20% and 10-50% in the near and far future, respectively. In contrast, the low discharges in the dry season are projected to decrease up to 13-30% as fewer wet days are expected. The uncertainty analysis reveals that climate models dominate the total projection uncertainty, although the contribution of hydrological models and extreme value distributions is not negligible. The results of this research contribute to the planning of climate change adaptation strategies and actions to reduce future risks.</p>

<p>To study climate change we rely on global climate models (GCMs) but their resolution is coarse to investigate impacts at the local scale. Hence, a downscaling task is required for the use of these coarse-resolution outputs. In this sense, statistical downscaling methods (SDMs) are commonly applied to analyse the local impacts. Furthermore, a quantification of the uncertainty share of the SDMs is advised to complement the results. However, many choices need to be done before their application and these decisions can bias the outcome of the analysis. This work examines the SDMs’ uncertainty share to evaluate to what extent the different adopted strategies can impact the climate change signal (CCS) associated with the study. For this, eleven research indicators (six representing precipitation extremes) are used with four future scenarios, 28 state-of-art GCMs, and 15 SDMs of two different types (change factor and quantile mapping methods). The uncertainty involved is quantified by the variance decomposition procedure. Three different decisions are tested:</p> <p>(i) The selection of the Coupled Model Intercomparison Project (CMIP) era. The uncertainty shares in phases five and six (CMIP5 and CMIP6, respectively) are compared.  </p> <p>(ii) The selection of the SDM ensemble based on the SDMs’ methodological construction. More specifically, based on an ensemble of five methods of change factor type (including an event-based change factor weather generator) and an ensemble of ten methods of quantile mapping.</p> <p>(iii) The selection of the optimal SDM ensemble number. Different unique SDMs combinations are tested from k-ensemble members in [2,n] with n as the ensemble with the largest number of members (n=15).</p> <p>To complement the analysis, the outcomes of the CCSs from all the combinations in (ii) and (iii) are analysed. The results showed that the uncertainty quantification of the SDMs is not sensitive to the selection of the CMIP era. However, this choice is important if the focus is on the GCMs and future scenarios. Hence, it is preferable (but not mandatory) to perform the analysis with the most recent era. The selection of the SDMs based on a methodological construction might bias the conclusions. Therefore, it is better to include methods from all possible types since the results showed that the more methods included in the downscaling, the more reliable the estimation of the SDMs’ uncertainty share. The CCS seems to strongly depend on the choice of the SDM ensemble, and it tends to converge from different k-ensemble members in [2,n] towards the largest ensemble (n). Hence, CCSs from large SDM ensembles will be more reliable. Future work must extend the analysis into different climatological regions and include more methods from all the possible types.</p>

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