![]() To date, different statistical downscaling techniques have been widely adopted in various studies, e.g. ![]() The statistical downscaling approach is effective and generally does not require huge computer resources. Then by assuming that the derived relationship is maintained with time, we can feed predictors of GCM outputs into the statistical model to obtain future climate information 10, 11. On the other hand, statistical downscaling amounts to firstly searching for a relationship between local observed variables (called predictands) and large-scale GCM climate predictors. Along with the massive requirement of computer resources and modeling time, the dynamical downscaling method still has to deal with biases and the sensitivity of the boundary conditions taken from the host GCM, as well as the accuracy and uncertainty of the dynamics and physical parameterization of each RCM 8. Dynamical downscaling is related to a modeling process that feeds the coarse-resolution initial conditions (IC) and lateral boundary conditions (LBC) provided by a GCM into a regional climate model (RCM) to produce higher-resolution climate information 8, 9. There are two popular downscaling techniques, namely dynamical and statistical, that have been widely used. Hence, downscaling techniques, which transform the coarse GCM information into a higher spatial resolution, should be conducted for a specific region of interest before conducting local impact assessment and risk management. Besides the coarse resolution, biases and uncertainties contained in GCMs often exaggerate from global to regional and local scales, restraining the usefulness and applicability of GCMs in small-scale studies 5– 7. To date, the resolution of the CMIP6 GCMs is still too coarse (usually over 100 km) to be used in many aspects such as risk assessment, adaptation management, and decision-making procedures at the regional or local scale. The CMIP6 future scenario experiments are classified into core priority groups, including 1) the tier-1 experiments with SSPs 1–2.6, 2–4.5, 3–7.0, and 5–8.5, and the tier-2 experiments with SSPs 1–1.9, 4-3.4, 4–6.0, and 5–3.4 4. The Shared Socioeconomic Pathways (SSPs) scenarios used in AR6 include the SSP1 (Sustainability), SSP2 (Middle of the road), SSP3 (Regional Rivalry), SSP4 (Inequality), and SSP5 (Fossil-fueled Development) pathways 3. The new state-of-the-art climate projections for the coming decades, made available by the CMIP Phase 6 (CMIP6) 1, provide the underlying scientific ground for the latest IPCC Sixth Assessment Report (AR6) 2 of the Intergovernmental Panel on Climate Change (IPCC). The Coupled Model Intercomparison Project (CMIP), established in 1995, is an effort of the World Climate Research Programme (WCRP) that produces and studies the outputs of Global Climate Models (GCMs) to better understand the past, present, and future of the climate system. Results indicated the good performance of CMIP6-VN for the historical period, suggesting that the dataset could be used for studies on climate change assessment and impacts in Vietnam. The new dataset is called CMIP6-VN, covering the present-time period 1980–2014 and future projections for 2015–2099 from both CMIP6 tier-1 (Shared Socioeconomic Pathways (SSPs) 1–1.26, 2–4.5, 3–7.0, and 5–8.5) and tier-2 (SSPs 1–1.9, 4–3.4, 4–6.0) experiments. The Bias Correction and Spatial Disaggregation (BCSD) method is adopted to bias-correct monthly GCM simulations using observation data, then subsequently temporally disaggregate them into daily data. In response to the needs in Vietnam, this study constructs a new precipitation and temperature daily dataset for Vietnam, at a high spatial resolution of 0.1° × 0.1°, based on the outputs of 35 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). High-resolution climate projections are mandatory for many applications and impact assessments in environmental and management studies.
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