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<p>Daily climate data at high spatial resolution is essential for impact assessment in mainland Southeast Asia, yet existing products either lack spatial detail or temporal coverage. We present MSEA-Daily, a gridded daily climate dataset at 4-km resolution spanning 1981–2014 for mainland Southeast Asia (Myanmar, Thailand, Laos, Cambodia, Vietnam). The dataset provides daily precipitation, maximum temperature, minimum temperature, and mean temperature that maintain strict consistency with TerraClimate monthly values while incorporating realistic daily variability from CHIRPS (precipitation) and AgERA5 (temperature). Independent validation against 77 GHCND stations across mainland Southeast Asia and peninsular Malaysia demonstrate moderate skill for precipitation and high-to-excellent skill for the three temperature variables, with minimum and mean temperature showing the strongest agreement against station observations. Residual systematic biases are dominated by a small cool bias in maximum temperature inherited from TerraClimate, near-zero bias in mean temperature, and spatially structured precipitation biases linked to orography. MSEA-Daily reproduces the seasonal cycle and the timing of monsoon onset and withdrawal across the dry, hot, rainy and transition seasons, although precipitation extremes at the 95th percentile and above are underestimated by approximately 10–15% relative to station observations. MSEA-Daily also reproduces the spatial pattern of the JJAS ENSO–monsoon teleconnection over mainland Southeast Asia, including the central Indochina dry/central Vietnamese wet precipitation dipole during El Niño years and the lagged temperature response over Indochina. The dataset enables integrated climate assessments requiring both monthly climatological consistency for trend analysis and daily resolution for process modeling, with primary applications in agricultural modeling, hydrological simulation, and climate extreme analysis.</p>

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MSEA-Daily: A 4-km daily climate dataset for mainland Southeast Asia (1981–2014)

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  • 发表日期：

    2026年6月19日

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  • Teerachai Amnuaylojaroen,
  • Atsamon Limsakul

摘要

Daily climate data at high spatial resolution is essential for impact assessment in mainland Southeast Asia, yet existing products either lack spatial detail or temporal coverage. We present MSEA-Daily, a gridded daily climate dataset at 4-km resolution spanning 1981–2014 for mainland Southeast Asia (Myanmar, Thailand, Laos, Cambodia, Vietnam). The dataset provides daily precipitation, maximum temperature, minimum temperature, and mean temperature that maintain strict consistency with TerraClimate monthly values while incorporating realistic daily variability from CHIRPS (precipitation) and AgERA5 (temperature). Independent validation against 77 GHCND stations across mainland Southeast Asia and peninsular Malaysia demonstrate moderate skill for precipitation and high-to-excellent skill for the three temperature variables, with minimum and mean temperature showing the strongest agreement against station observations. Residual systematic biases are dominated by a small cool bias in maximum temperature inherited from TerraClimate, near-zero bias in mean temperature, and spatially structured precipitation biases linked to orography. MSEA-Daily reproduces the seasonal cycle and the timing of monsoon onset and withdrawal across the dry, hot, rainy and transition seasons, although precipitation extremes at the 95th percentile and above are underestimated by approximately 10–15% relative to station observations. MSEA-Daily also reproduces the spatial pattern of the JJAS ENSO–monsoon teleconnection over mainland Southeast Asia, including the central Indochina dry/central Vietnamese wet precipitation dipole during El Niño years and the lagged temperature response over Indochina. The dataset enables integrated climate assessments requiring both monthly climatological consistency for trend analysis and daily resolution for process modeling, with primary applications in agricultural modeling, hydrological simulation, and climate extreme analysis.