Distribution Of Precipitation Research Articles

Data-driven analysis of annual rain distributions

Rainfall is an important component of the climate system, and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well. Published by the American Physical Society 2024

The effect of quantitative traits on grain productivity of maize hybrids depending on climatic conditions

The purpose of the study was to identify the effect of quantitative traits on grain productivity of maize hybrids depending on moisture availability. The study was carried out on the breeding field of the laboratory for maize breeding and seed production of the FSBSI “ARC “Donskoy” in 2020–2023. The soil of the experimental plot was ordinary blackearth, with a humus layer thickness of 140 cm. The climate is characterized by aridity with unstable moisture. The years of the study were contrasting in temperature and moisture availability (HTC 0.49–0.83). Due to the uneven distribution of precipitation during a vegetation period of maize, the temperature regime through the months and moisture availability had even greater differences (HTC 0.11–2.28). The objects of research were 96 maize hybrids. The main method for developing hybrids was an interline hybridization. There has been identified a stable correlation between grain productivity and quantitative traits in each of the four years of study, when ‘one ear weight’ was r = 0.34±0.09 – 0.64±0.08 and ‘number of grains in a ear row’ was r = 0.35±0.09–0.58±0.08. The specific climatic conditions of some years of the study (one year out of four) influenced the lack of correlation between amount of productivity and quantitative traits with ‘number of ears per plant’ (r = 0.11 ± 0.10 in 2022), ‘number of grains per ear’ (r = 0.01±0.10 in 2023), ‘grain yield during threshing (r = 0.04±0.10 in 2023). The correlation between productivity and traits was subject to even greater variability depending on weather conditions with ‘ear attachment height’ (r = 0.01±0.10–0.40±0.09), ‘plant height’ (r = 0.16±0.10–0.38±0.09), ‘ear diameter’ (r = -0.02±0.10–0.43±0.09), ‘1000-grain weight’ (r = -0.14±0.10–0.45±0.09). In all years of the study, there were no correlation between productivity and such traits as ‘number of grain rows’ (r = -0.27±0.09–0.17±0.10) and ‘ear length’ (r = 0.01±0.10–0.29±0.09).

Global Survey of Energetic Electron Precipitation at Low Earth Orbit Observed by ELFIN

AbstractWe statistically evaluate the global distribution and energy spectrum of electron precipitation at low‐Earth‐orbit, using unprecedented pitch‐angle and energy resolved data from the Electron Losses and Fields INvestigation CubeSats. Our statistical results indicate that during active conditions, the ∼63 keV electron precipitation ratio peaks at L > 6 at midnight, whereas the spatial distribution of precipitating energy flux peaks between the dawn and noon sectors. ∼1 MeV electron precipitation ratio peaks near midnight at L > ∼6 but is enhanced near dusk during active times. The energy spectrum of the precipitation ratio shows reversal points indicating energy dispersion as a function of L shell in both the slot region and at L > ∼6, consistent with hiss‐driven precipitation and current sheet scattering, respectively. Our findings provide accurate quantification of electron precipitation at various energies in a broad region of the Earth's magnetosphere, which is critical for magnetosphere‐ionosphere coupling.

Extracting Latent Variables From Forecast Ensembles and Advancements in Similarity Metric Utilizing Optimal Transport

AbstractThis study presents a novel methodology for extracting latent variables from high‐dimensional sparse data, particularly emphasizing spatial distributions such as precipitation distribution. This approach utilizes multidimensional scaling with a distance matrix derived from a new similarity metric, the Unbalanced Optimal Transport Score (UOTS). UOTS effectively captures discrepancies in spatial distributions while preserving physical units. This is similar to mean absolute error, however it considers location errors, providing a more robust measure crucial for understanding differences between observations, forecasts, and ensembles. Probability distribution estimation of these latent variables enhances the analytical utility, quantifying ensemble characteristics. The adaptability of the method to spatiotemporal data and its ability to handle errors suggest its potential as a promising tool for diverse research applications.

Elevation dependent climate assessment and its influence on snow cover variability in Hindu Kush Himalayan region using Google Earth Engine for the period of 2003–2021

The Hindu Kush Himalayan (HKH) regions are taken for this study, which is predominantly characterized by amplified climatic uncertainty and rapid snow melting. Because at the present there is a lack of studies estimating the elevation dependent and climate influenced snow cover variability in the HKH region. This present study examines elevation-dependent (2003–2021) distribution and trends in precipitation, temperature, and snow cover area (SCA) by employing numerous non-parametric statistical applications using the Google Earth Engine (GEE). The Modified Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover data is employed to estimate SCA along with the Climate Hazards Group Infrared Precipitation with Station Data version 2.0 (CHIRPS-V2.0) for precipitation and MODIS land surface temperature (LST). The study reveals statistically significant decreasing annual, seasonal, and monthly trends of SCA in most of the elevation zones typically during winter season (−0.256%/year), whereas temperature and precipitation exhibit strong spatial variability. Inclusive investigation portrays maximum mean annual and seasonal increased precipitation at the low-elevated regions, whereas seasonal disparities in temperature ostensible marginal decreased at high-elevation, albeit increased at lowland. Moreover, topographic importance on SCA distribution showcases maximum snow (43.4%) at north-facing less surface inclination with flat topography (14.6%). The comprehensive elevation wise analysis improves our understanding of the overall hydrological challenge and assist in the development of more reliable flood forecasting and water resource management.

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

Intra-annual dynamics of stem circumference variation and water status of four coniferous tree species (Pinus sylvestris, Picea abies, Larix decidua and Abies alba) under warmer and water-limited conditions

AbstractIn the paper, the intra-annual growth patterns and tree water balance of four different tree species (Pinus sylvestris, Picea abies, Larix decidua and Abies alba) were examined. Seasonal radial increment, tree water deficit (ΔW) and maximum daily shrinkage (MDS) were derived from the records obtained using high-resolution digital band dendrometers. The study area was located in Arboretum Borová hora (350 m a. s. l., Zvolen valley, Central Slovakia) characterised by a warmer climate (Picea abies) and warmer and drier climate (Abies alba, Larix decidua, Pinus sylvestris) compared to the sites of tree origins. Monitored species exhibited remarkably distinct growth and water balance patterns over the 2015 growing season characterised with the highly above normal temperature and uneven precipitation distribution. A. alba exhibited smooth continuous growth least affected by varying environmental conditions. Of all analysed species, only A. alba showed significant positive correlations of radial growth and ΔW with temperature and global radiation, despite environmental water limitations. The lowest cumulative growth, lower negative values of ΔW and greater MDS of L. decidua indicate a higher water limitation of this species. The results showed more pronounced sensitivity of P. sylvestris to increased temperature and drought. All monitored variables of environmental conditions, except precipitation, significantly influenced MDS values of all studied tree species. Based on 30 variables describing radial stem growth patterns and water status we identified large inter-species variability and discrete species-specific groups, while the indicators of growth and water status of L. decidua and P. sylvestris were similar and the most different patterns were observed between A. alba and L. decidua. The behaviour of P. abies was closer to A. alba than to the other two species.

Accuracy of historical precipitation from CMIP6 global climate models under diversified climatic features over India

The importance of global climate models (GCMs) is increasingly recognized due to their excellent ability to accurately predict climatic factors. These capabilities prove invaluable to water resources engineers as they facilitate effective planning and strategic decision-making. Finally, evaluating the performance of GCMs is very important because it allows us to simulate and predict different climate scenarios, empowering us to make informed choices. Therefore, the purpose of this study is to determine the degree of discordance between historical simulated data produced by the CMIP6 models and historical observational data over different climate zones of India. The ability of 24 different GCMs to reproduce the geographical and seasonal distribution of Indian precipitation has been tested by analyzing the daily historical precipitation forecasts from these models. These models have been used to estimate the degree of uncertainty associated with the spatiotemporal variability of precipitation forecasts. More than 20% percent bias (PBIAS) is observed to occur predominantly in four climate classifications: polar tundra, temperate, cold, and tropical monsoon. In some regions of India, the CMIP6 models produce overestimated or underestimated results. The locations identified indicate that there have been changes of more than 20% PBIAS near Sivalik Range, Naga Hills, and Western Ghats. The precipitations of those regions that have been underestimated also imply that those locations have different climatic conditions. This study also highlights that CMIP6 GCMs are yet to produce better results near several Indian mountainous regions depending upon climates. The outcomes of this study will be very useful for reconstructing modeled data for that specific regions.

Spatial and Temporal Evolution of Precipitation in the Bahr el Ghazal River Basin, Africa

Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention.

Enhancing Soil Resilience to Climatic Wetting‐Drying Cycles Through a Bio‐Mediated Approach

AbstractClimatic wetting‐drying cycles exacerbated by climate change can trigger several weakening mechanisms in surface soils, potentially leading to instability and failure of slopes and earthen structures. This study proposes a bio‐mediated approach based on microbially induced calcite precipitation (MICP) to increase soil resilience to wetting‐drying cycles. To explore its viability and the underlying mechanisms, we conducted a series of laboratory tests on clayey soil that underwent six wetting‐drying cycles. The tests were conducted with different treatment methods to investigate the effect of treatment sequence and cementation solution concentration. After MICP treatment, the initial evaporation rate, surface crack ratio during drying, and total soil weight loss during rainfall erosion were reduced by up to 32%, 85%, and 90%, respectively. Spraying the cementation solution first in the MICP treatment sequence proves more effective in improving soil water retention capacity. On the other hand, initiating the sequence with the bacterial solution demonstrates a more pronounced effect in reducing soil desiccation cracks and erosion. Microstructure analysis reveals that the content and distribution of CaCO3 precipitation are the major factors controlling the effectiveness of MICP for the cementation of clayey soil. Employing MICP can minimize the carbon footprint and contribute to developing environmentally friendly solutions for soil improvement in regions affected by climatic wetting‐drying cycles.

Reproducibility of Equatorial Kelvin Waves in a Superparameterized MIROC: 1. Implementation and Verification of Blockwise‐Coupled SP‐MIROC

AbstractThe potential scope of superparameterization (SP) was extended to higher resolutions of the global climate model (GCM) component by devising a technique called blockwise coupling. In this method, a horizontal average of multiple GCM columns, instead of one, is coupled to a cloud‐resolving model (CRM) domain. This enables SP‐GCMs to reduce the computational cost drastically, enabling higher‐resolution GCMs to be superparameterized. A blockwise‐coupled SP‐GCM called SP‐MIROC was implemented by coupling the climate model MIROC6 to the CRM SCALE‐RM. The 4 × 4‐bundled SP‐MIROC successfully reproduced horizontal patterns and frequency distributions of precipitation and realistic amplitudes of equatorial Kelvin waves (EKWs), which were underestimated in the standard MIROC6. As discussed in Yamazaki and Miura (2024b, https://doi.org/10.1029/2023MS003837) of this study, the amplitude boost of EKWs was enabled by a top‐heavy heating in SP‐MIROC. Comparison of power spectra between the 4 × 4‐bundled SP‐MIROC and nonbundled SP‐MIROC indicated that the effective resolution of dynamic variables was not degraded by the blockwise technique. Rather, spectra in the 4 × 4‐bundled SP‐MIROC were more realistic than those in the nonbundled SP‐MIROC. Although the 4 × 4‐bundling limits convective coupling in the smallest GCM scale, it could offer the best match of resolutions between the GCM‐handled dynamics and SP‐derived physics because the effective resolution of the dynamics is lower than the nominal grid spacing.

Self-clustered GAN for precipitation nowcasting

This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets.

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

Investigating the Impact of Large Lakes on Local Precipitation: Case Study of Lake Urmia, Iran

Large lakes face considerable challenges due to human activities and climate change, impacting local weather conditions and ecosystem sustainability. Lake Urmia, Iran’s largest lake and the world’s second-largest saltwater lake, has undergone a substantial reduction in water levels, primarily due to drought, climate change, and excessive irrigation. This study focuses on the potential repercussions on local climate conditions, particularly investigating the impact of moisture sources, evaporation from lake surfaces, and evapotranspiration from agricultural activities, on local convection rainfall. The prevailing westerly winds in the basin suggest a hypothesis that this moisture is transported eastward within the basin, potentially leading to local precipitation as it ascends to higher altitudes near the eastern basin border. To validate this hypothesis, climate data from 1986 to 2017 from the Sarab meteorological station (east of the lake basin, influenced by local precipitation) and Saqez meteorological station (south of the basin, unaffected by local precipitation) were analyzed. The impact of lake water level reduction was assessed by categorizing data into periods of normal lake conditions (1986–1995) and water level reduction (1996–2017). Additionally, the MSWEP global precipitation product was used to examine the precipitation distribution in the entire basin over the entire period and sub-periods. The findings indicate Lake Urmia’s significant influence on convective rainfall in the eastern basin, especially during the summer. Despite decreasing lake levels from 1996 to 2017, convective rainfall in the eastern basin increased during the summer, suggesting intensified agricultural irrigation, particularly in hot seasons.

Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations

Accurate precipitation data holds immense significance in hydrological analysis. A common challenge in this field often stems from the lack of comprehensive data availability. High-resolution satellite-based precipitation measurements covering large areas offer a potential solution. However, disparities in the resolution of observed rainfall data can impact data accuracy. The main goal of this study is to evaluate the accuracy of rainfall data obtained from the TRMM and GPM satellites in the Kuranji watershed. The evaluation was conducted on the performance of the GPM IMERG-F from the Integrated Multi-satellite Retrievals for the GPM mission and the TRMM 3B42RT on a daily scale spanning from 2015 to 2019 over the Kuranji watershed. The daily precipitation measurements were validated using three widely used statistical metrics (R, RMSE, and RB). The precipitation detection capability (POD, FAR, and CSI) was also considered in this assessment. The findings demonstrate that both satellite estimations exhibit a substantial correlation coefficient (0.68 for GPM, 0.62 for TRMM) with the measurements obtained from gauges, along with an inclination to overestimate precipitation. GPM IMERG-F and TRMM 3B42RT manifest a consistent spatial pattern in daily precipitation distribution, effectively representing the observed precipitation distribution. The greater probability of detection (POD), critical success index (CSI), and lower false alarm ratio (FAR) exhibited by GPM IMERG-F at varying rainfall intensities suggests its superior performance in accurately identifying observed precipitations. This finding supports the preference for GPM IMERG-F data over TRMM 3B42RT data across various applications, hydrology, and related disciplines in the future.

Precipitation Changes on the Northern Slope of the Kunlun Mountains in the Past 42 Years

The precipitation on the northern slope of the Kunlun Mountains significantly impacts the green economy of the Tarim Basin’s southern edge. Observations have noted an expansion of the surface water area in this region, though the reasons for this are not yet fully understood. Due to limited instrumental data, this study leverages field measurements from the third Xinjiang comprehensive expedition and multiple gridded datasets. Through trend analysis and a geographical detector model, it examines the precipitation’s decadal, interannual, and seasonal variations across key areas (Hotan River Basin, Keriya River Basin, Qarqan River Basin, and Kumukuli Basin), identifying factors behind the spatial and temporal distribution of regional precipitation. The findings reveal the following: (1) An increase in annual precipitation across the region from 187.41 mm in the 1980s to 221.23 mm in the early 21st century, at a rate of 10.21 mm/decade, with the most significant rise in the eastern Kunlun-Kumukuli Basin. (2) Precipitation exhibits clear seasonal and spatial patterns, predominantly occurring in spring and summer, accounting for 90.27% of the annual total, with a general decrease from the mountains towards downstream areas. (3) Rising average annual temperatures contribute to an unstable atmospheric structure and increased water-holding capacity, facilitating precipitation. Significant influences on precipitation changes include the North Atlantic Oscillation and solar flux, explaining 43.98% and 31.21% of the variation, respectively.

Investigation on the Sensitivity of Precipitation Simulation to Model Parameterization and Analysis Nudging over Hebei Province, China

The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus “gray-zone” resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications.

Effects of heat treatment on the nano-indentation, corrosion and cavitation erosion behavior of 17-4PH stainless steel by laser surface melting

The effects of heat treatment on the microstructure, residual stresses, nano-indentation behavior, the resistance to the corrosion and cavitation erosion (CE) of 17-4PH stainless steel (17-4PH SS) by laser surface melting (LSM) were thoroughly investigated. Experimental results showed that the samples were mainly composed of martensite phase, and the NbC precipitations formed after heat treatment. The distribution of NbC precipitations was more uniform in the sample (S3) after solution treatment 3 h at 1040 °C + aging treatment 3 h at 480 °C. The grain size was increased for the samples after heat treatment. This heat treatment processing significantly reduced the residual stresses generated from the LSM process, and the nano-indentation behavior of the LSM samples after heat treatment was improved compared to that of the LSM sample without heat treatment. The S3 sample displayed the highest corrosion resistance as indicated by the low corrosion current density (icorr), high pitting potential (Epit) and high polarization resistance (Rp). The CE resistance of samples after heat treatment in 3.5 wt% NaCl solution was higher than that of the sample without heat treatment. Among them, the S3 sample exhibited the highest CE resistance, and its CE mass loss was only 59.5 % of the sample without heat treatment. The excellent CE resistance resulted from the appropriate combination of mechanical properties, corrosion resistance and self-recovery ability of the passivation film.

Research on the Causes and Prevention Measures of Urban Waterlogging

Flood disaster is one of the major natural disasters in China, due to the influence of monsoon climate, seasonal distribution of precipitation and unbalanced spatial distribution, urban flood disasters in China are prone to occur frequently, endangering the safety of life and property of urban residents, and also affecting the safe operation and sustainable development of cities. Therefore, the prevention and control of urban waterlogging is not only a major livelihood project, but also a major development project. In view of the repeated failures of urban waterlogging prevention and control in China, this paper analyzes the causes of urban waterlogging from four aspects: climate change, urban expansion, drainage system, and emergency rescue, analyzes the mechanism of urban waterlogging from the perspective of disaster system theory, and finally summarizes the coping strategies of urban waterlogging based on four aspects: stormwater management, institutional mechanism, engineering construction, and emergency management, in order to provide reference and reference for urban waterlogging control.

Meteorological disaster disturbances on the main crops in the north‒south transitional zone of China

Global climate change, with warming as its main feature, has altered the spatial-temporal evolution of factors such as precipitation and temperature that can cause meteorological disasters. The complex and changeable climate has led to frequent natural disasters, while the frequency and intensity of extreme climate events have also significantly increased, posing an enormous threat to societal production and human life. As the most important geoecological transitional zone of mainland China, the stability of agricultural production in China’s north–south transitional zone is crucial for ensuring food security under climate change. With the use of daily precipitation and potential evapotranspiration data from 1961 to 2018, this study focused on analysing disturbances such as extreme precipitation and drought disasters at different time scales during the winter wheat and summer maize growing seasons in the north–south transitional zone of China from an agricultural production perspective and attempted to answer the following questions: first, from an agricultural production perspective, what are the temporal and spatial distribution patterns of extreme precipitation and arid climate events in the north–south transitional zone? Second, which areas are at high risk of being disturbed by different types of meteorological disasters and require increased attention? The results indicated that (1) in terms of the overall temporal variation, the degree of extreme precipitation and drought stress faced by agricultural production in the region is decreasing. However, the temporal variation at each station in the north–south transitional zone was not completely consistent with the overall trend, and both increasing and decreasing trends were observed. The sites exhibiting an increase overlapped with typical regions of the north–south transitional zone to varying degrees, indicating that the typical regions represented not only theoretical potential risk areas under climate change but also suffered from meteorological disaster disturbances. (2) The precipitation distribution during the winter wheat growth period in the south–north transitional zone was uneven and varied significantly. High values of extreme precipitation indices during the winter wheat growth period were mainly concentrated in the southern part of the eastern section of the north‒south transitional zone. The precipitation distribution during the summer maize growth period significantly differed, with the highest amount of heavy rain and largest number of rainstorm days concentrated in the southeastern part of the north‒south transitional zone. The spatial distribution of the drought frequency in the north–south transitional zone, as indicated by the monthly standardized precipitation evapotranspiration index (SPEI1), showed that the areas with high total drought frequencies were mainly concentrated in northeast Jiangsu, southeast Henan, and north Anhui, which primarily experienced light drought. The central part of Jiangsu Province exhibited a high frequency of moderate drought, while southern Jiangsu Province and southwestern Shaanxi Province were prone to severe drought. Additionally, southeastern Hebei and eastern Henan were identified as areas with a high frequency of extreme drought. Finally, the central region of Sichuan Province was characterized by both severe and extreme drought conditions. Based on the SPEI12-derived spatial distribution of the drought frequency in the north–south transitional zone, the areas with a high total drought frequency were mainly concentrated in central and eastern Henan, southeast Shaanxi, southeast Shandong, and central Sichuan, which primarily experienced light to moderate drought. The northwestern part of Jiangsu, the southern part of Hebei, and the western part of Shandong are regions with a high frequency of severe drought, while the eastern part of Henan is an area with high frequencies of both severe and extreme drought. (3) High-value areas of extreme precipitation and drought disturbance in the north–south transitional zone overlapped with the edge of the transitional zone to varying degrees. Approximately 63.58% of the north‒south transitional zone of China was characterized by moderate or high stress levels, primarily concentrated along the southern boundary and central core area, and nearly 39.5% of all counties experienced two or more types of disaster stresses.