Atmospheric Variability Research Articles

An absence of binary companions to Wolf-Rayet stars in the Small Magellanic Cloud

To predict black hole mass distributions at high redshifts, we need to understand whether very massive single stars (M ≳ 40 M⊙) with low metallicities (Z) lose their hydrogen-rich envelopes, like their metal-rich counterparts, or whether a binary companion is required to achieve this. To test this, we undertook a deep spectroscopic search for binary companions of the seven known apparently single Wolf-Rayet (WR) stars in the Small Magellanic Cloud (SMC; where Z ≃ 1/5 Z⊙). For each of them, we acquired six high-quality VLT-UVES spectra spread over a time period of 1.5 years. By using the narrow N V lines in these spectra, we monitored radial velocity (RV) variations to search for binary motion. We find low RV variations of between 6 and 23 km/s for the seven WR stars, with a median standard deviation of 5 km/s. Our Monte Carlo simulations imply probabilities below ∼5% that any of our target WR stars have a binary companion more massive than ∼5 M⊙ with orbital periods of less than a year. We estimate that the probability that all our target WR stars have companions with orbital periods shorter than 10 yr is below ∼10−5 and argue that the observed modest RV variations may originate from intrinsic atmosphere or wind variability. Our findings imply that metal-poor massive stars born with M ≳ 40 M⊙ can lose most of their hydrogen-rich envelopes via stellar winds or eruptive mass loss, which strongly constrains their initial mass–black hole mass relation. We also identify two of our seven target stars (SMC AB1 and SMC AB11) as runaway stars with a peculiar RV of ∼80 km/s. Moreover, with all five previously detected WR binaries in the SMC exhibiting orbital periods of less than 20 d, a puzzling absence of intermediate-to-long-period WR binaries has emerged, with strong implications for the outcome of massive binary interactions at low metallicities.

A New Method for Calculating Instantaneous Atmospheric Heat Transport

Abstract Atmospheric heat transport (AHT) is an important piece of our climate system but has primarily been studied at monthly or longer time scales. We introduce a new method for calculating zonal-mean meridional AHT using instantaneous atmospheric fields. When time averaged, our calculations closely reproduce the climatological AHT used elsewhere in the literature to understand AHT and its trends on long time scales. In the extratropics, AHT convergence and atmospheric heating are strongly temporally correlated suggesting that AHT drives the vast majority of zonal-mean atmospheric temperature variability. Our AHT methodology separates AHT into two components (eddies and the mean meridional circulation) which we find are negatively correlated throughout most of the mid- to high latitudes. This negative correlation reduces the variance in the total AHT compared to eddy AHT. Last, we find that the temporal distribution of the total AHT at any given latitude is approximately symmetric.

Spatio‐Temporal Characteristics and Variability of GNSS‐Derived Atmospheric Precipitable Water Vapor From 2010 to 2023 in Fujian Province, China

AbstractAtmospheric precipitable water vapor (PWV) is a crucial factor affecting precipitation and the atmospheric environment. To quantitatively investigate the spatio‐temporal distribution and characteristics of the PWV in Fujian, China, its diurnal and seasonal variations are analyzed based on the global navigation satellite system (GNSS) data from 2010 to 2023, ground meteorological observations, meteorological sounding data and ERA5 data. Moreover, the spatio‐temporal characteristics of the monthly PWV are assessed by using the empirical orthogonal function (EOF), the Mann‐Kendall test and the sliding t‐test. The results indicate that the ground‐based GNSS PWV data is able to reveal the distribution and variation of PWV over Fujian. Specifically, the diurnal distribution of the PWV varies remarkably with time, and the PWV in the eastern coastal areas is generally higher than in the western mountainous areas. The seasonal variation characteristics of the PWV are consistent with the atmospheric circulation variations, with the largest amount of PWV in summer, followed by spring, autumn and winter. Moreover, the EOF spatial modes show that the PWV distributions are different in the eastern coastal areas and inland mountainous areas. The oscillation intensity of the PWV strengthens from the northwest to the southeast, and the corresponding time series of the PWV displays apparent seasonal variations. The observed precipitation is inconsistent with the PWV and is more affected by local terrain and thermodynamic conditions. The Mann‐Kendall test and the sliding t‐test indicate that the PWV over Fujian has not undergone abrupt changes during the past 13 years, but there is a possibility of sudden changes in the future.

Detecting changes in anthropogenic light emissions: Limits due to atmospheric variability

Monitoring the evolution of the anthropogenic light emissions is a priority task in light pollution research. Among the complementary approaches that can be adopted to achieve this goal stand out those based on measuring the direct radiance of the sources at ground level or from low Earth orbit satellites, and on measuring the scattered radiance (known as artificial night sky brightness or skyglow) using networks of ground-based sensors. The terrestrial atmosphere is a variable medium interposed between the sources and the measuring instruments, and the fluctuation of its optical parameters sets a lower limit for the actual source emission changes that can be confidently detected. In this paper we analyze the effect of the fluctuations of the molecular and aerosol optical depths. It is shown that for reliably detecting changes in the anthropogenic light emissions of order ∼1 % per year, the inter-annual variability of the annual means of these atmospheric parameters in the measurement datasets must be carefully controlled or efficiently corrected for.

Atmospheric Water Vapor Variability over Houston: Continuous GNSS Tomography in the Year of Hurricane Harvey (2017)

Atmospheric Water Vapor Variability over Houston: Continuous GNSS Tomography in the Year of Hurricane Harvey (2017)

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

Different sensitivity of postglacial vegetation to climate and human disturbance between Tibetan Plateau and its surrounding regions

Characterizing the ecosystems' response to environmental changes on Tibetan Plateau (TP), as well as elucidating the regional disparities of these changes across the plateau's surface and its neighboring regions, holds significance for safeguarding the ecological security of Asia. Here, an improved pollen-based biomization procedure, incorporating weighted taxa, was conducted to illustrate the spatial and temporal differences of vegetation changes in TP, Xinjiang and southwestern China over the past 21,000 years. Results showed that the vegetation changes in TP and Xinjiang were characterized by alternations among herbaceous biomes, while the southwestern China were featured by changes among various woody biomes. Asynchronous vegetation change rates (ROCs) were discovered among TP, Xinjiang and southwestern China. The ROCs in TP were generally low during the Last Glacial Maximum but rose abruptly and peaked in the late deglacial period, whereas Xinjiang and southwestern China had high and increasing ROCs during Holocene. Those trajectories might relate to different sensitivities of vegetation to climate change and human disturbances. Habitat limitation on TP should be pretty intense during glacial period, leading to rapid vegetation establishment after glacier retreat. While in Xinjiang, water availability was the most obvious limitation, making the vegetation change sensitive to moisture and atmospheric CO2 variations. The forest in Yunnan behaved the most stability but was characterized by vertical migration responding to deglacial warming. Holocene acceleration of ROCs aligned with intensified human activities in Xinjiang and southwestern China. To sum up, environmental conditions have been always important in determining post glacial vegetation changes in west China, but the ROCs were asynchronous due to different sensitivities of vegetation to climate change and human activity. Although the plateau's vegetation was quite stable during Holocene, the plateau terrestrial ecosystem may subject to increasing risk under accelerating global warming and intensifying human activities.

Radiological impact assessment of a dirty bomb using Hotspot Code and AI: Insights from spent nuclear fuel isotopes

This study evaluates the potential radiological effects resulting from the use of a Radiological Dispersal Device (RDD), commonly known as a “dirty bomb,” in Dhamar City, Yemen, focusing on its impact on human health. Seasonal atmospheric variations were analyzed using the HotSpot Health Physics Code to model the Total Effective Dose Equivalent (TEDE) distribution following an explosion. The TEDE’s effect on different human organs was assessed, considering both external and internal radiation exposures. The study demonstrated that winter nights posed the highest radiation risk, with dose distribution areas of 0.018 km2, 0.14 km2, and 3.3 km2 for inner, middle, and outer zones, respectively, surpassing other seasonal and daily values. Conversely, summer recorded the lowest radiation spread. A detailed temporal analysis during winter nights showed an initial TEDE of 9.5 Sv at 0.1 min post-explosion, which decreased exponentially to 1.3 Sv after 2000 min (33.3 h) and further to 0.0038 Sv after 6500 min (108.3 h) with the aid of AI-enhanced “Long Short Term Memory Networks (LSTM)”. The highest radiation doses to human organs within a 100 m2 area during the initial 10 min post-detonation were significantly above permissible limits, with critical organs like the surface bone, liver, and red marrow receiving doses of 68 Sv, 12 Sv, and 5.5 Sv, respectively. These findings underline the need for robust emergency response strategies and long-term health monitoring to mitigate the adverse health impacts of RDDs. The comprehensive assessment presented in this study, enhanced by artificial intelligence, provides essential data for improving public health safety and emergency preparedness in regions susceptible to radiological threats.

Contribution of internal variability to the Mongolian Plateau summer precipitation trends in MPI-ESM large-ensemble model

Summer precipitation over the Mongolian Plateau (MP) has experienced a consistent decline in recent decades. While the influence of atmospheric wave train on this reduction in precipitation has been recognized in prior studies, this study delves deeper into the physical mechanisms and quantifies the contributions of the internal atmosphere and oceanic variations to the diminishing precipitation utilizing a comprehensive 100-member ensemble simulations from the Max Planck Institute Earth System Model (MPI-ESM). Results show that the ensemble-mean precipitation in MP exhibits a positive trend and cannot explain the observed results. The precipitation trends vary significantly among individual ensemble members, highlighting the pivotal role of internal variability. The leading EOF mode of precipitation trends among ensemble members exhibits uniform variations. Further investigations reveal that the internal summer precipitation in MP is affected by the internal atmospheric circulation, the remote influence of the North Atlantic Dipole sea surface temperature (SST) anomalies, and Pacific Decadal Oscillation-like SST patterns. An eastward-propagating Rossby wave originating from the North Atlantic dipole SST anomalies provides the anomalous large-scale circulation that influences summer precipitation. The PDO contributes to reinforcing the anticyclonic anomaly over the MP. Additionally, the uncertainty of precipitation trends in MPI-ESM can be reduced by 13% through removing the internal atmospheric wave train-related precipitation variation, while oceanic factors only contribute about 7% uncertainty of precipitation variations. Our insights enhance the understanding of the physical drivers behind summer precipitation variability in the MP and effectively quantify the uncertainties stemming from internal variability.

A Puzzling Quasi‐Periodic Variability in the Tropical Middle Atmosphere

AbstractThe Quasi‐Biennial Oscillation and the Semiannual Oscillation have been identified to be the leading modes of variability in the tropical middle atmosphere. With reanalysis data and independent rocket soundings from a low latitude site, we report the existence of yet another variability in the tropical lower mesosphere which is primarily evident as easterly bursts in zonal winds during the months of May‐July. It occurs with a variable interval of 2–5 yrs in the late 20th century and 7–9 yrs in the early 21st century. These Quasi‐Periodic Easterly Bursts are found to have remote influences on the Antarctic polar vortex as well as residual circulation in the lower mesosphere. We identify a potential causative mechanism for the easterly bursts that involve enhanced cross equatorial advection of momentum as well as gravity wave drag. A close association with Quasi Biennial Oscillation winds is observed, however, cause of the observed periodicity remains elusive.

The Influence of Atmospherics on Consumer Decision-Making Among Windhoek Restaurant Customers

The purpose of this research was to establish the atmospheric variables used by restaurants, and to determine the relationship between atmospherics and subliminal and supraliminal stimuli. The study explored how atmospherics influenced the absolute threshold and investigated how atmospherics (pleasure, arousal, and dominance) affect the choices made by the chosen Windhoek restaurant customers. The study made use of a simple random sampling and the questionnaires were distributed to customers who dined at the said restaurant during a six-month period. The sample response rate was about 60% and yielded 175 responses. SPSS Inc Version 28 of 2021 and SPSS Amos version 26 were used for data analysis. Descriptive statistics were analysed, using factor analysis and Structural Equation Modelling (SEM) to display the data. The study established a relationship between atmospherics and subliminal as well as supraliminal stimuli. Marketing scholars are urged to psychology experts to gain greater insights on the phenomenon.

Advancing Parsimonious Deep Learning Weather Prediction Using the HEALPix Mesh

AbstractWe present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3‐hr time resolution for up to 1‐year lead times on a 110‐km global mesh using the Hierarchical Equal Area isoLatitude Pixelization (HEALPix). In comparison to state‐of‐the‐art (SOTA) machine learning (ML) weather forecast models, such as Pangu‐Weather and GraphCast, our DLWP‐HPX model uses coarser resolution and far fewer prognostic variables. Yet, at 1‐week lead times, its skill is only about 1 day behind both SOTA ML forecast models and the SOTA numerical weather prediction model from the European Center for Medium‐Range Weather Forecasts. We report several improvements in model design, including switching from the cubed sphere to the HEALPix mesh, inverting the channel depth of the U‐Net, and introducing gated recurrent units (GRU) on each level of the U‐Net hierarchy. The consistent east‐west orientation of all cells on the HEALPix mesh facilitates the development of location‐invariant convolution kernels that successfully propagate weather patterns across the globe without requiring separate kernels for the polar and equatorial faces of the cube sphere. Without any loss of spectral power after the first 2 days, the model can be unrolled autoregressively for hundreds of steps into the future to generate realistic states of the atmosphere that respect seasonal trends, as showcased in 1‐year simulations.

Managing Green and Sustainable Technologies: Climate-Informed Corrosion Prediction for Steel Structures

The unpredictability of atmospheric circumstances is one of the major elements that contribute to the capability to anticipate the corrosion growth in metal structures over time accurately. Climate shifts can potentially modify the long-term attributes of these factors throughout the operational life of metal structures, both those currently in existence and those newly developed. The impact of climate irregularity on the probabilistic nature of atmospheric variables, which significantly impact corrosion situations, can add intricacy to corrosion predictions in these constructions. This project presents an incorporated framework to quantify the impact of climate alteration on the corrosion rates of steel structures in Jordan. It considers the changes in environmental conditions, specifically temperature, relative humidity, and wind speed, and their impacts on atmospheric corrosion. Global Climate Models are employed to assess the long-term effects of climate transformation on these environmental circumstances. An analytical model for anticipating corrosion rate is integrated with climate transformation models to predict modifications in the corrosion rates of steel parts relative to historical situations. This project also examines the impact of climate transformation on the fluctuations of these climatic parameters and offers a contrast between historical data and projected conditions across the country. The findings indicate a significant increase in corrosion rates across Jordan, which calls for localized green building codes and standards to ensure that future infrastructure is sustainable and capable of withstanding the new climatic norms. This approach addresses the immediate challenges posed by climate change and contributes to the broader goals of sustainable urban development and managing green technology adoption in Jordan. Doi: 10.28991/CEJ-2024-010-08-016 Full Text: PDF

Short‐Term to Inter‐Annual Variability of the Non‐Migrating Tide DE3 From MIGHTI, SABER, and TIDI: Potential Tropospheric Sources and Ionospheric Impacts

AbstractUpward propagating waves of lower atmospheric origin play an important role in coupling terrestrial weather with space weather on daily to inter‐annual timescales. Quantifying their short‐term (<30 days) variability is a difficult challenge because simultaneous observations at multiple local times are needed to sample diurnal cycles. This study demonstrates and validates a short‐term estimation method of the DE3 non‐migrating tide at the equator and then applies the technique to three independent data sets: MIGHTI, SABER, and TIDI. We find that daily DE3 estimates from SABER, MIGHTI, and TIDI at equator agree well with correlation coefficients ranging between 0.76 and 0.85. The daily DE3 amplitude variability is typically ∼7 m/s in zonal winds and ∼3 K in temperature. We also find that daily MLT variations and F‐region ionospheric DE3 from COSMIC‐2 Global Ionospheric Specification (GIS) show a correlation of 0.55–0.65, suggesting that not all ionospheric variability can be attributed to the E‐region dynamo; however, increasing correlation with increasing time‐scale suggests that lower atmospheric variability has pronounced impact on the ionosphere on intra‐seasonal scales. We find that the MLT and the F‐region ionosphere exhibit strong coherent intra‐seasonal oscillations (residual amplitudes upto 50%–60%); their coherency with the MJO in 2020 suggests a possible modulation of the upward propagating DE3 tide related to this major tropical tropospheric weather pattern. In addition, we find stratospheric QBO signatures in the MLT DE3 on inter‐annual scales. This study offers fresh observational insights into the pivotal role of tropospheric weather in shaping variability in the coupled thermosphere‐ionosphere system.

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

Mean flow and eddy summer moisture transport over East Asia in reanalysis data and a regional climate simulation

Understanding the impact of atmospheric variability on climatological mean moisture transport is crucial because moisture transport determines continental water availability as well as convective organization and resulting precipitation. Here, we analyze the mean flow and eddy components of summer moisture transport in the downwind of the Tibetan Plateau (TP), a region that is characterized by interactions between monsoon systems, extratropical circulation, and mountainous weather systems. Using 40 years of ERA5 reanalysis data and a regional WRF simulation, we determine the absolute and relative contributions of mean flow and eddy moisture transport from multi-daily to sub-daily scales. We also link these components to large-scale circulation indices, precipitation, evaporation, and mesoscale convective systems (MCSs). The results show that the largest contributions of eddies to the climatological mean moisture transport are found in the immediate downwind region of the TP. Half of the total eddy transport downwind of the TP is due to multi-daily eddy transport and the other half is due to daily to sub-daily eddy transport. Regional precipitation anomalies are dominated by the mean flow component of southerly moisture influxes which in turn are positively correlated with different South Asian summer monsoon indices and negatively correlated with the West Northern Pacific monsoon index. The eddy transport from the south is positively correlated with a lower jet latitude but does not show any significant correlations with precipitation or MCS activity, likely due to the dominant role of the mean flow moisture transport. While the relative contributions of eddies to the climatological mean moisture transport are similar in ERA5 and WRF, the correlations between moisture transport components and large-scale circulation indices are generally weaker in WRF. This suggests that the dynamical downscaling does not significantly change the role of eddy moisture transport averaged for the region, but it resolves processes that decouple the moisture transport from its large-scale forcing.

A machine learning model that outperforms conventional global subseasonal forecast models

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF’s state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.

Geographic variability in dust and temperature in climate scaling regimes over the Last Glacial Cycle

Abstract. Temperature and mineral dust records serve as valuable palaeoclimatic indicators for studying atmospheric variability across different temporal scales. In this study, we employed Haar fluctuations to analyse global spatiotemporal atmospheric variability over the Last Glacial Cycle, capturing both high- and low-frequency information within the records, regardless of uniform or non-uniform sampling. Furthermore, we utilised Haar fluctuations to compute fluctuation correlations, thereby enhancing our understanding of palaeoclimate dynamics. Our findings reveal a latitudinal dependency in the transition from macroweather to climate regimes (τc), with polar regions experiencing shorter transitions compared to the tropics and mid-latitudes. These transitions occur at approximately 1/100th of glacial cycle length scales, suggesting a dominant forcing mechanism beyond Milankovitch cycles. Additionally, our analysis shows that polar regions have larger fluctuation amplitudes than lower latitudes as a consequence of the polar amplification effect. Furthermore, fluctuation correlations demonstrate faster synchronisation between the poles themselves compared to lower-latitude sites, achieving high correlation values within 10 kyr. Therefore, our findings suggest a consistent climate signal propagating from the poles to the Equator, representing the first empirical evidence supporting the hypothesis that the poles play a pivotal role as climate change drivers, influencing the variability in climatic transitions worldwide.

Multidecadal variability from ocean to atmosphere in the North Atlantic: Perturbation potential energy as the bridge

Abstract The North Atlantic Ocean forcings are considered an important origin of the North Atlantic atmospheric multidecadal variability. Here we reveal the energetics mechanisms of the phenomenon using the perturbation potential energy (PPE) theory. Supporting the previous model studies, a cyclic pattern involving the Atlantic multidecadal oscillation (AMO) and North Atlantic tripole (NAT) is observed: positive AMO phase (AMO+, similarly hereafter) →NAT−→AMO−→NAT+, with a phase lag of approximately 15~20 years. An atmospheric mode characterized by basin-scale sea level pressure anomaly in the North Atlantic is associated with the AMO, which is termed the North Atlantic uniformity (NAU). The AMO+ induces positive uniform PPE anomalies over the ocean through precipitation heating, leading to decreased energy conversion to perturbation kinetic energy (PKE) and a large-scale anomalous cyclone. For the NAT+, tripolar precipitation anomalies result in tripolar PPE anomalies. Anomalous energy conversions occur where the PPE anomaly gradient is large, explained by an energy balance derived from thermal wind relationship. The PKE around 15°N and 50°N (25°N and 75°N) increases (decreases), forming the anomalous anticyclone and cyclone at subtropical and subpolar region, respectively, known as the North Atlantic Oscillation (NAO). The reverse holds for the NAT− and AMO−. As the phases of the ocean modes alternate, the energetics induce the NAU−, NAO−, NAU+, and NAO+ sequentially. In the multidecadal cycle, the accumulated energetics process is related to delayed effect, and the difference in variance explanation between the NAU and NAO is attributed to the feedback mechanisms.

Deep learning-based bias correction of ISMR simulated by GCM

Simulated data of atmospheric variables like precipitation is very important for climate science research, especially for understanding future scenarios. Such data is generated by global or regional climate models (GCM/RCM), for both past and future periods under different initial conditions. Unfortunately, these models frequently display biases in simulated precipitation data compared to ground observations, due to their inability to incorporate the entire physics of the process accurately. It is imperative to mitigate such biases using contemporary techniques to make the simulated data a valuable end product. Our aim in this research is to correct seasonal (from June to September) Indian Summer Monsoon Rainfall (ISMR) data as simulated by the Climate Forecast System (CFS) over the Indian subcontinent, with the Global Precipitation Climatology Project (GPCP) data serving as the ground truth reference. This period is important as more than one-third of India's annual rainfall occurs during these months. This study presents a novel Deep Learning (DL) based architecture known as the Convolutional Neural Network for Bias Correction (CNNBC) that aims to calibrate the model-simulated data with past observations, to address these biases while preserving the statistical properties and spatial correlations relationships among grid points and enhancing the spatial mean of precipitation estimates. To evaluate the effectiveness of our proposed method, we compare its performance to that of three other statistical and DL techniques: quantile mapping (QM), quantile delta mapping (QDM), and the SRDRN model. The comparative analysis demonstrates that the CNNBC model outperforms the other in terms of our task.