Regional Precipitation Research Articles

Added value of EURO-CORDEX downscaling over the complex orography region of the Pyrenees

This study presents an assessment of the added value of downscaling utilizing Regional Climate Models (RCMs) compared to Global Climate Models (GCMs) in the high mountain region of the Pyrenees, characterized by complex topography. The EURO-CORDEX ensemble was investigated, employing a gridded high-resolution observational database as a reference. A recently proposed method is applied to quantify the performance gains or losses associated with dynamic downscaling. Our analysis focuses on calculating the added value by exploring the extremes of the Probability Density Function (PDF), spatial distribution patterns, and its relationship with elevation. Overall, our findings reveal significant improvements in the representation and general characterization of precipitation, minimum temperature, and maximum temperature in the Pyrenean region. Furthermore, RCMs demonstrate enhanced performance in capturing maximum precipitation events; however, they struggle to represent low precipitation rates, particularly in the Mediterranean area of the mountain range. Regarding temperature extremes, dynamical downscaling exhibits improvements in capturing maximum events. Nevertheless, deficiencies are observed in the RCMs’ representation of minimum temperature events for both minimum and maximum temperature variables, as well as in representing near-freezing temperatures.

Analysis of the Relationship between Extreme Precipitation Pattern and Maximum Runoff in Hongze County From the Perspective of Urban Underlying Surface Production and Confluence

Based on the hourly precipitation data and evaporation data of the meteorological in Hongze County from 1959 to 2020, we studied the relationship between extreme precipitation and annual runoff control rate from the perspective of the underlying surface of the Hongze County watershed. We conducted a literature review and statistical analysis using SPSS in this study. From 1959 to 2020, precipitation in the Hongze region varied greatly annually, with the maximum and minimum annual precipitation of 1473.8 and 349.6 mm. The precipitation fluctuated but increased, and the extreme precipitation was concentrated in July to August. The annual average evapotranspiration from 1959 to 2020 was 1165.8 mm with an inter-annual fluctuation and decrease due to the urbanization process. The process affected the local evapotranspiration condition. More evaporation was observed from May to August than from December to January with a difference of 50 mm. The evaporation data coincided with seasonal variation in Hongze County. In determining the control rate of annual total runoff, we considered the inter-annual and annual differences in precipitation. The natural runoff condition and economic cost before urbanization were explored, too. The greater the rain intensity, the earlier the runoff generation time, the shorter the confluence time, and the greater the possibility of floods. In short-duration extreme precipitation, it is necessary to pay attention to its middle period, while for long-duration extreme precipitation, it is necessary to focus on its late period.

More Heavy Precipitation in World Urban Regions Captured Through a Two‐Way Subgrid Land‐Atmosphere Coupling Framework in the NCAR CESM2

AbstractCurrent global climate models (GCMs), limited to grid‐scale land‐atmosphere coupling, cannot represent subgrid urban‐rural precipitation contrasts. This study develops an innovative two‐way subgrid land‐atmosphere coupling framework in the National Center for Atmospheric Research (NCAR) Community Earth System Model version 2 (CESM2) to explicitly resolve land‐atmosphere interaction over subgrid individual land units. Results show that urban heat island (UHI) leads to the urban rainfall effect (URE), which in turn alleviates overestimated UHI over China in CESM2. The URE manifests as a shift toward more heavy precipitation and less light precipitation in world urban areas than in surrounding rural counterparts. This feature is consistent with available observations. In heavy precipitation situations, the UHI promotes atmospheric instability and enhances atmospheric water vapor holding capacity, resulting in more heavy precipitation in urban areas. Conversely, in light precipitation situations, the UHI and decreased evaporation from urban impermeable surfaces diminish atmospheric relative humidity, suppressing light precipitation.

Testing the Sensitivity of a WRF-Based Great Lakes Regional Climate Model to Cumulus Parameterization and Spectral Nudging

Abstract The Weather Research and Forecasting (WRF) Model is used to dynamically downscale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2-m temperature and precipitation accumulation in the Great Lakes drainage basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated. Significance Statement Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

Impacts of Moisture Advection Scheme on Precipitation in the Steep Topography Region between the Tibetan Plateau and the Sichuan Basin

Abstract Water vapor transport is a crucial process in modeling and can contribute to errors in precipitation forecasts. To investigate the sensitivity of precipitation to the moisture advection scheme, this study introduced the two-step shape-preserving advection scheme (TSPAS), which has been proven to improve precipitation simulation over steep topography at lower resolutions, into the Southwest Center Weather Research and Forecast (WRF)-based Intelligent Numerical Grid Forecast System (SWC-WINGS) at a convection-permitting resolution. According to experiments conducted throughout the summer of 2021, the precipitation over the eastern slope of the Tibetan Plateau (TP) is highly sensitive to the moisture advection scheme. TSPAS successfully improved precipitation over the eastern slope of the TP, especially for torrential rainfall. The fractions skill score (FSS) is improved by 0.075 (27.78%) for daily precipitation with a threshold of 100 mm. Compared with the experiment with the original WRF advection scheme, the TSPAS reduced the overestimation of precipitation in the topographic region and excessive water vapor transport in a low-level atmosphere. To understand the precipitation improvement contributed by the advection scheme, additional experiments were conducted for a particular precipitation process from two approaches: switching advection schemes during the rainfall evolution and updating the variables related to moisture advection individually. Results demonstrate that the precipitation improvement is mainly contributed by the moisture advection scheme before the precipitation. Among the different variables, the combination of wind and water vapor was the most influential factor causing the precipitation improvement under the TSPAS.

The Shrinking Great Salt Lake May Exacerbate Droughts by Reducing Local Precipitation: A Case Study

Abstract The Great Salt Lake (GSL) is a shallow terminal lake located in northern Utah, United States. Over the years, the water extent of the GSL has undergone substantial reduction due to water diversions and a changing climate—in particular rising temperatures. However, the potential impacts of the shrinking GSL water body on the local hydroclimate system are poorly understood. In this study, we utilized the Weather Research and Forecasting Model, version 4.2, coupled with a lake model to simulate a series of high-resolution numerical experiments; these experiments aimed to assess the effect of varying lake areal extents on a storm event that occurred on 6 June 2007. The results revealed a systematic decline in the quantity of precipitation over the GSL and downwind regions with declining areal coverage. In the event of complete disappearance, the regional average precipitation would experience an approximate 50% reduction relative to its 2004 base lake extent; this decrease is principally attributed to a diminished water vapor flux and moist static energy (MSE) above the lake. The research underscores the consequences of a shrinking GSL, not just for precipitation delivery downstream but that of a negative feedback loop within the hydroclimatic system of the GSL basin, i.e., water flow reductions into the basin.

A Downscaling Method of TRMM Satellite Precipitation Based on Geographically Neural Network Weighted Regression: A Case Study in Sichuan Province, China

Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, the existing methods often fail to adequately consider the spatial heterogeneity and complex nonlinearity between precipitation and surface parameters, resulting in poor downscaling performance and inaccurate expression of regional details. In this study, we propose a precipitation downscaling model based on geographically neural network weighted regression (GNNWR), which integrates normalized difference vegetation index, digital elevation model, land surface temperature, and slope data to address spatial heterogeneity and complex nonlinearity. We explored the spatiotemporal trends of precipitation in the Sichuan region over the past two decades. The results show that the GNNWR model outperforms common methods in downscaling precipitation for the four distinct seasons, achieving a maximum R2 of 0.972 and a minimum RMSE of 3.551 mm. Overall, precipitation in Sichuan Province exhibits a significant increasing trend from 2001 to 2019, with a spatial distribution pattern of low in the northwest and high in the southeast. The GNNWR downscaled results exhibit the strongest correlation with observed data and provide a more accurate representation of precipitation spatial patterns. Our findings suggest that GNNWR is a practical method for precipitation downscaling considering its high accuracy and model performance.

A dataset of downscaled monthly precipitation at 1 km resolution in Guangxi, China and ASEAN regions from 2001 to 2020

Located in the southeastern Asia, Guangxi, China and the ASEAN regions have a subtropical monsoon climate with abundant rainfall and frequent floods, which has a great impact on social and economic activities. The precipitation data with high precision and high spatial and temporal resolution are of great significance for industrial and agricultural production, water conservancy development, drought and flood monitoring and prevention, and ecological environment protection. In this paper, we used the Global Precipitation Measurement Mission precipitation data (GPM IMERG) from 2001 to 2020 as the dependent variable in combination with the enhanced vegetation index (EVI), surface evapotranspiration (ET), land surface temperature (LST) from MODIS data and elevation (ELV) from ASTER data as explanatory variables. Then we introduced a geographically weighted regression (GWR) model to construct an annual scale model, depicting the spatial variation of 10-km satellite precipitation with the influence of geographical environmental conditions. Five types of kernel functions were adopted in our GWR model, including the gaussian, exponential, bisquare, tricube, and boxcar kernel function. The optimal kernel function was selected based on the correlation coefficient, the root means square error and bias. Thus, we established the dataset of downscaled monthly precipitation in Guangxi, China and ASEAN regions from 2001 to 2020 by extrapolating the GWR model with input variables at 1 km resolution. The dataset of 1-km monthly precipitation from 2001 to 2020 was also generated by the proportional coefficient method. Moreover, ground observation data from 2,679 stations during 2001–2020 were used for verification. The correlation coefficient, root mean square error and deviation were 0.792, 74.610mm and -0.122%, respectively. The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations at 1-km resolution in detail, and could be potentially promising for ecological environment, hydrological management, flood prediction and other relevant fields.

Prakiraan Jumlah Curah Hujan Berdasarkan Data Parameter Cuaca Tahun 2011-2021 di Kota Pangkalpinang

Tropical weather prevails in the Pangkalpinang city region, complemented with a changeable rainy season that experiences monthly variations. This phenomena results in a considerable amount of precipitation in the city of Pangkalpinang, making the area known for its extreme rainfall. Using multiple linear regression approaches to anticipate monthly precipitation is the aim of this work. Rainfall (RR), air temperature (T), air humidity (RH), and air pressure are all part of the dataset that was collected over an 11-year period (2011–2021). The information was obtained from the meteorological station in Depati Amir Pangkalpinang. This study uses the multiple linear regression method. The rainfall forecast results are compared to the observed actual rainfall amounts. Based on air temperature and air humidity as predictors, the results show that the estimation of cumulative monthly precipitation in the Pangkalpinang city region for the year 2021 produced an average root mean square error (RSME) value of 112.6 mm/month. Furthermore, the T-test analysis produced a 0.9 result. With air temperature, humidity, and pressure as predictors, the total monthly rainfall for 2021 was predicted with an average root mean square error (RMSE) value of 107.9 mm/month. A T-test was also performed, and the result was 0.4. The results of this study show that the monthly rainfall forecast model for the city of Pangkalpinang that includes factors related to temperature, humidity, and pressure produces better results than the model that only includes these variables. November and December are the months with the greatest variations.

Statistical Interdependence between Daily Precipitation and Extreme Daily Temperature in Regions of Mexico and Colombia.

We study the statistical interdependence between daily precipitation and daily extreme temperature for regions of Mexico (14 climatic stations, period 1960-2020) and Colombia (7 climatic stations, period 1973-2020) using linear (cross-correlation and coherence) and nonlinear (global phase synchronization index, mutual information, and cross-sample entropy) synchronization metrics. The information shared between these variables is relevant and exhibits changes when comparing regions with different climatic conditions. We show that precipitation and temperature records from La Mojana are characterized by high persistence, while data from Mexico City exhibit lower persistence (less memory). We find that the information exchange and the level of coupling between the precipitation and temperature are higher for the case of the La Mojana region (Colombia) compared to Mexico City (Mexico), revealing that regions where seasonal changes are almost null and with low temperature gradients (less local variability) tend to display higher synchrony compared to regions where seasonal changes are very pronounced. The interdependence characterization between precipitation and temperature represents a robust option to characterize and analyze the collective dynamics of the system, applicable in climate change studies, as well as in changes not easily identifiable in future scenarios.

Influence of SMAP soil moisture retrieval assimilation on runoff estimation across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

Responses of precipitation and water vapor budget on the Chinese Loess Plateau to global land cover change forcing

There have been notable changes in precipitation patterns on the Loess Plateau (LP) of China in recent decades, and numerous attribution studies have focused on sea surface temperature anomalies and atmospheric circulation changes induced by aerosols and greenhouse gases emission. However, the influences of global land use and land cover change (LULCC) as an important forcing factor in the climate system on regional precipitation remains poorly understood. In this study, we quantified the impacts of LULCC on precipitation and the water vapor budget in the LP region, utilizing data from LULCC forcing experiments conducted by the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Although global LULCC forcing exerted a negative effect on long-term mean precipitation on the LP region from 1850 to 2014, the different response characteristics were detected during different time periods. The global LULCC caused a decrease of 14 mm in annual precipitation during the period of 1850–1960. Conversely, from 1961 to 2014, it led to an increase of 6.4 mm, which is largely attributed to the enhanced water vapor transport along the southern boundary and westerly belt of the LP region. Moreover, from the perspective of the net water vapor balance of the entire LP, although LULCC caused net water vapor export during both periods 1850–1960 and 1961–2014, the export during the latter period (0.20 × 104 kg s−1) was smaller than that during the former period (0.28 × 104 kg s−1), indicating that the global expansion of grassland and cropland, along with the continuous rise in the leaf area index from 1961 to 2014, contributed to retaining more water vapor within the LP, which in turn was more favorable for precipitation. These findings provide valuable insights into the reasons behind precipitation variations in the LP region, emphasizing that global vegetation restoration and greening play a significant role in improving precipitation in ecologically fragile areas.

Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases

Abstract. Reliable attribution of Asian summer monsoon variations to aerosol forcing is critical to reducing uncertainties in future projections of regional water availability, which is of utmost importance for risk management and adaptation planning in this densely populated region. Yet, simulating the monsoon remains a challenge for climate models that suffer from long-standing biases, undermining their reliability in attributing anthropogenically forced changes. We analyze a suite of climate model experiments to identify a link between model biases and monsoon responses to Asian aerosols and associated physical mechanisms, including the role of large-scale circulation changes. The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model's ability to simulate the spatio-temporal variability in the climatological monsoon winds, clouds, and precipitation across Asia, which modulates the magnitude and efficacy of aerosol–cloud–precipitation interactions, an important component of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response. We found a striking contrast between the early- and late-summer aerosol-driven changes ascribable to opposite signs and seasonal evolution of the biases in the two regions. A realistic simulation of the evolution of the large-scale atmospheric circulation is crucial to realize the full extent of the aerosol impact over Asia. These findings provide important implications for better understanding and constraining the diversity and inconsistencies of model responses to aerosol changes over Asia in historical simulations and future projections.

“Mega-sandwich pattern” of interdecadal precipitation variations and its regional manifestation in the Asian summer precipitation region

“Mega-sandwich pattern” of interdecadal precipitation variations and its regional manifestation in the Asian summer precipitation region

Multiscale Interactions Driving Summer Extreme Precipitation in Central Asia

AbstractThis study identified four patterns of regional extreme precipitation events (REPEs) in Central Asia (CA) and their crucial synoptic systems and multiscale interactions. Four patterns with distinct spatial distributions were identified in: northern Kazakhstan, southern Xinjiang, western CA, and the Tianshan Mountains. Focusing on the three most frequent REPEs, the kinetic energy (KE) cross‐scale transfer from the basic‐to synoptic‐scale windows exhibited a zonal dipole, resulting in the development and enhancement of REPEs in northern Kazakhstan. The available potential energy (APE) cross‐scale transfer exhibited opposing patterns between the upper and lower troposphere, indicating baroclinic instability in the lower troposphere and barotropic instability of the basic flow in the upper troposphere. Both mechanisms enhanced the Central Asian vortices (CAVs) in southern Xinjiang and induced REPEs. Conversely, the energy budgets exhibited baroclinic instability of the basic flow throughout the entire region when the Tianshan Mountains REPEs occurred, providing energy for prevalent CAVs.

Hysteresis of European summer precipitation under a symmetric CO2 ramp-up and ramp-down pathway

This study investigates the mechanism of the hysteresis of European summer mean precipitation in a CO2 removal (CDR) simulation. The European summer mean precipitation exhibits robust hysteresis in response to the CO2 forcing; after decreasing substantially (∼40%) during the ramp-up period, it shows delayed recovery during the ramp-down period. We found that the precipitation hysteresis over Europe is tied to the hysteresis in the Atlantic Meridional Overturning Circulation (AMOC). During the ramp-down period, an anomalous high surface pressure circulation prevails over Europe. The anomalous high pressure system is a baroclinic response of the atmosphere to strong North Atlantic cooling associated with a weakened AMOC. This anomalous circulation suppresses summertime convective activity over the entire Europe by decreasing near-surface moist enthalpy in Central and Northern Europe while increasing lower free-tropospheric temperature in Southern Europe. Our findings underscore the need to understand complex interactions in the Earth system for reliable future projections of regional precipitation change under CDR scenarios.

Identifying clusters of precipitation for the Brazilian Legal Amazon based on magnitude of trends and its correlation with sea surface temperature

Prioritizing watershed management interventions relies on delineating homogeneous precipitation regions. In this study, we identify these regions in the Brazilian Legal Amazon based on the magnitude of Sen’s Slope trends using annual precipitation data from September to August, employing the Google Earth Engine platform. Utilizing the silhouette method, we determine four distinct clusters representing zones of homogeneous precipitation patterns. Cluster 0 exhibits a significant median increase in precipitation of 3.20 mm year−1 over the period from 1981 to 2020. Cluster 1 shows a notable increase of 8.13 mm year−1, while Clusters 2 and 3 demonstrate reductions in precipitation of − 1.61 mm year−1 and − 3.87 mm year−1, respectively, all statistically significant. Notably, the region known as the arc of deforestation falls within Cluster 2, indicating a concerning trend of reduced precipitation. Additionally, our analysis reveals significant correlations between Sea Surface Temperature (SST) in various oceanic regions and precipitation patterns over the Brazilian Legal Amazon. Particularly noteworthy is the strong positive correlation with SST in the South Atlantic, while negative correlations are observed with SST in the South Pacific and North Atlantic. These findings provide valuable insights for enhancing climate adaptation strategies in the Brazilian Legal Amazon region.

Changes of soil carbon along precipitation gradients in three typical vegetation types in the Alxa desert region, China

The changes and influencing factors of soil inorganic carbon (SIC) and organic carbon (SOC) on precipitation gradients are crucial for predicting and evaluating carbon storage changes at the regional scale. However, people’s understanding of the distribution characteristics of SOC and SIC reserves on regional precipitation gradients is insufficient, and the main environmental variables that affect SOC and SIC changes are also not well understood. Therefore, this study focuses on the Alxa region and selects five regions covered by three typical desert vegetation types, Zygophyllum xanthoxylon (ZX), Nitraria tangutorum (NT), and Reaumuria songarica (RS), along the climate transect where precipitation gradually increases. The study analyzes and discusses the variation characteristics of SOC and SIC under different vegetation and precipitation conditions. The results indicate that both SOC and SIC increase with the increase of precipitation, and the increase in SOC is greater with the increase of precipitation. The average SOC content in the 0–300cm profile is NT (4.13 g kg−1) > RS (3.61 g kg−1) > ZX (3.57 g kg−1); The average value of SIC content is: RS (5.78 g kg−1) > NT (5.11 g kg−1) > ZX (5.02 g kg−1). Overall, the multi-annual average precipitation (MAP) in the Alxa region is the most important environmental factor affecting SIC and SOC.

California wildfire smoke contributes to a positive atmospheric temperature anomaly over the western United States

Abstract. Wildfires in the southwestern United States, particularly in northern California (nCA), have grown in size and severity in the past decade. As they have grown larger, they have been associated with large emissions of absorbing aerosols and heat into the troposphere. Utilizing satellite observations from MODIS, CERES, and AIRS as well as reanalysis from MERRA-2, the meteorology associated with fires during the wildfire season (June–October) was discerned over the nCA-NV (northern California and Nevada) region during the period 2003–2022. Wildfires in the region have a higher probability of occurring on days of positive temperature (T) anomalies and negative relative humidity (RH) anomalies, making it difficult to discern the radiative effects of aerosols that are concurrent with fires. To attempt to better isolate the effects of large fire emissions on meteorological variables, such as clouds and precipitation, variable anomalies on high fire emission days (90th percentile) were compared with low fire emission days (10th percentile) and were further stratified based on whether surface relative humidity (RHs) was anomalously high (75th percentile) or low (25th percentile) compared with typical fire season conditions. Comparing the simultaneously high fire emission and high RHs data with the simultaneously low fire emission and high RHs data, positive tropospheric T anomalies were found to be concurrent with positive AOD anomalies. Further investigation found that due to shortwave absorption, the aerosols heat the atmosphere at a rate of 0.041 ± 0.016 to 0.093 ± 0.019 K d−1, depending on whether RH conditions are anomalously positive or negative. The positive T anomalies were associated with significant negative 850–300 hPa RH anomalies during both 75th percentile RHs conditions. Furthermore, high fire emission days under high RHs conditions are associated with negative CF anomalies that are concurrent with the negative RH anomalies. This negative CF anomaly is associated with a significantly negative regional precipitation anomaly and a positive net top-of-atmosphere radiative flux anomaly (a warming effect) in certain areas. The T, RH, and CF anomalies under the simultaneously high fire emission and high RHs conditions compared with the simultaneously low fire emission and high RHs conditions have a significant spatial correlation with AOD anomalies. Additionally, the vertical profile of these variables under the same stratification is consistent with positive black carbon mass mixing ratio anomalies from MERRA-2. However, causality is difficult to discern, and further study is warranted to determine to what extent the aerosols are contributing to these anomalies.

Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China

Subseasonal precipitation forecasting is challenging but critical for water management, energy supply, and disaster prevention. To improve regional subseasonal precipitation prediction, previous studies have proposed a dynamical-statistical projection model (DSPM). In this study, we develop a new method that combines the DSPM and deep learning (DL), called the DL-DSPM. The DSPM is developed using the observed relationships between large-scale atmospheric circulations and regional precipitation, and the dynamical forecasted atmospheric circulations from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The DL-DSPM improves upon the DSPM by correcting biases in atmospheric circulation forecasts from the ECMWF model using two DL models, namely, residual network and U-Net models. In the case of Southwest China (SWC), DL models can improve atmospheric circulation forecasts at lead times beyond 5 pentads, including large-scale drivers of SWC precipitation variability. The DL-DSPM outperforms the ECMWF model and DSPM forecasts in predicting precipitation anomalies beyond 4 and 5 pentads over most SWC regions, respectively. In addition, the DL-DSPM is more skillful than the ECMWF model and DSPM in predicting extreme precipitation events more than 4 pentads in advance. The successful combination of DL and the DSPM provides a new possible direction for DL applications in subseasonal precipitation forecasting.