Introduction
Precipitation forecasting is an ardous problem of forecasting the specific range of region rainfall based on current observations, from sources including radar [ 47 ; 54 ] , satellites [ 28 ] , and rain gauges [ 54 ] .
Forecasting plays a vital role in society, aiding in a wide range of weather-dependant decision-making, including large-scale crop management, marine services, and air traffic control [ 72 ; 69 ; 37 ; 23 ] .
A Numerical Weather Prediction (NWP) model [ 64 ] is a general tool for weather forecasting, which involves calculating complex physical processes pertaining to interactions across large time and space scales, spanning the Earth’s atmosphere, ocean, land, and ice [ 36 ; 26 ; 4 ] .
Despite continuous efforts to enhance NWP models [ 57 ] , erroneous predictions can occur due to the delicacy of the models with respect to the noise in the initially observed state.

The weather and climate research community is becoming more aware of contemporary deep learning (DL) technologies, and numerous attempts have been made to apply them to the task of precipitation forecasting.
Most DL-based methods directly utilize radar observations to predict future precipitation. Despite the lack of expert knowledge or explicit physics-based modeling, these methods achieve notable performance while being computationally efficient [ 56 ; 1 ] .
Ravuri et al. [ 47 ] facilitate the performance of observation-driven methods with a deep generative model that learns probability distributions of the data and allow for easy generation of samples from their learned distributions.
Espeholt et al. [ 16 ] extend the capability of precipitation forecasting up to twelve hours ahead, surpassing the performance of existing physics-based models widely used in the Continental United States.
Despite its significance for instantaneous forecasting, it is shown that pure observation-driven DL models underperform those that are supplemented by NWP outputs when lead time is larger than 6 hours, revealing the limitations of DL in long-term forecasting.
Furthermore, it still lacks explainability and explicit consideration for physics constraints in the end-to-end DL framework [ 53 ] .

This paper presents a novel benchmark dataset for a hybrid workflow that combines the strengths of both NWP and DL approaches.
Our new benchmark, termed KoMet ( Ko rea Met eorological dataset ), consists of two parts: predictions from an NWP model, specifically, the Global Data Assimilation and Prediction Systems-Korea Integrated Model (GDAPS-KIM), and Automatic Weather Station (AWS) observations, whose rainfall amounts are hourly monitored in areas covering 13km around the center. The data is provided by the Korean Meteorological Administration (KMA), for July and August of 2020 and 2021–two months in which most precipitation in the Korean Peninsula occurs.
Our dataset takes into account of the characteristics of the Korean Peninsula whose precipitation is difficult to predict due to congested spatio-temporal variability owing to complex terrain and the Asian monsoon season [ 42 ; 9 ] . For example, its complex topography leads to large variation in annual precipitation in the southern part (1000 mm to 1800 mm) than the central part (1100 mm to 1400 mm) [ 2 ] . Under the proposed workflow, GDAPS-KIM predictions are used as inputs to a DL model which is trained to output refined precipitation forecasts. AWS observations are used as ground-truth targets to train the deep model.
In nutshell, the overall goal is to post-process the predictions from GDAPS-KIM using deep models, under the supervision of AWS observations.

To date, many benchmark datasets have been introduced to facilitate existing approaches in precipitation forecasting–those based on NWP and end-to-end DL [ 70 ; 44 ] .
We advocate an alternative approach, in which NWP output is post-processed using DL models for a more refined prediction. This enables the prediction to respect physics-related constraints and is partially explainable by the injected NWP predictions. To catalyze future work, we also provide extensive evaluation of several baseline models and learning methods over our KoMet dataset. Our key contributions include:

KoMet is the most comprehensive public dataset for DL post-processing of NWP outputs. The spatial size of GDAPS-KIM is 50 x 65 covering the terrain and surrounding sea whose each pixel covers 12km 2 and 484 AWS (ground-truth pixels) are installed in such resolution.

Hybrid NWP-DL Baselines . We present and analyze a comprehensive set of baseline methods for our proposed hybrid approach, and provide a suite of Python modules designed to facilitate further research from the ML community.