2026-10-14T14:40:27Z https://www.scidb.cn/oai

10.57760/sciencedb.39686 2026-06-12T11:53:19Z

2026-06-12 MSEA-Daily: temporal-disaggregation code for the MSEA-Daily 4-km daily climate dataset doi:10.57760/sciencedb.39686 en This deposit contains the complete Python processing pipeline used to generate the MSEA-Daily 4-km daily climate dataset for mainland Southeast Asia (1981–2014; archived at Science Data Bank, https://doi.org/10.57760/sciencedb.36048). The code temporally disaggregates TerraClimate monthly precipitation totals and monthly mean / maximum / minimum 2-m air-temperature fields to a daily time step at 1/24° (~4 km) horizontal resolution over the 4–24°N / 92–110°E domain, while preserving the original TerraClimate monthly totals (for precipitation) and monthly means (for temperature) exactly by construction. Daily temporal structure is supplied by CHIRPS v2.0 for precipitation — sharpened by a power transformation with exponent α = 1.35 calibrated against GHCND validation stations to better represent monsoon wet-day intensity — and by AgERA5 zero-mean daily anomalies for the three temperature variables. The pipeline covers source-data retrieval (including a cdsapi wrapper for AgERA5), grid harmonisation, precipitation and temperature disaggregation, post-production quality assurance, and ACDD-1.3 / CF-1.8-compliant NetCDF-4 packaging. All scripts are written in Python 3.10+, depend only on widely used scientific-Python packages (numpy, xarray, netCDF4, pandas, scipy, cdsapi), and are released under the MIT License to enable independent reproduction of MSEA-Daily, transparent inspection of every methodological choice documented in the accompanying Scientific Data descriptor, and adaptation of the disaggregation framework to other regions, time periods, or updated source datasets. mainland Southeast Asia; temporal disaggregation; daily climate dataset; TerraClimate; CHIRPS; AgERA5; GHCND; precipitation; air temperature; monthly-conservation constraint; zero-mean anomaly method; power-transformation disaggregation; reproducibility; open-source Python; NetCDF; ACDD-1.3; CF-1.8 Teerachai Amnuaylojaroen PUBLIC https://api.github.com/licenses/mit dataset Science Data Bank