As our planet is entering into the "global boiling" era, understanding
regional climate change becomes imperative. Effective downscaling methods that
provide localized insights are crucial for this target. Traditional approaches,
including computationally-demanding regional dynamical models or statistical
downscaling frameworks, are often susceptible to the influence of downscaling
uncertainty. Here, we address these limitations by introducing a diffusion
probabilistic downscaling model (DPDM) into the meteorological field. This
model can efficiently transform data from 1{\deg} to 0.1{\deg} resolution.
Compared with deterministic downscaling schemes, it not only has more accurate
local details, but also can generate a large number of ensemble members based
on probability distribution sampling to evaluate the uncertainty of
downscaling. Additionally, we apply the model to generate a 180-year dataset of
monthly surface variables in East Asia, offering a more detailed perspective
for understanding local scale climate change over the past centuries.

通过引入扩散概率降尺度模型（DPDM），从 1° 分辨率高效地变换数据至 0.1° 分辨率，本研究解决了传统降尺度方法在面对不确定性时的局限性，并将该模型应用于东亚生成 180 年的月表面变量数据集，为理解过去几个世纪以来的当地尺度气候变化提供了更详细的视角。