58 results about "Climate forecast" patented technology

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention discloses a marine climate extension scale prediction method based on artificial intelligence, and particularly relates to the field of marine climate prediction, and the method comprises the steps: fusing and standardizing multi-source spatio-temporal data, extracting multi-scale features, generating an extreme event sample, carrying out the combined training, and generating an adaptive optimization prediction result. According to the marine climate extension scale prediction method based on artificial intelligence, through a cascade space-time attention mechanism, prediction error amplification caused by insufficient cross-scale interaction is effectively reduced; on the basis of a conditional variation auto-encoder, historical sparse samples are supplemented through physical encoding and a residual error correction mechanism, the dependence of the model on the coverage degree of a training set is reduced, and the generalization ability of extreme event features is improved; a feature incidence matrix of atmosphere and ocean modes is dynamically calculated by using a gating circulation unit, and a conservation law loss function is introduced to constrain a feature evolution direction, so that the relevance and physical consistency of cross-scale features are enhanced.

The invention provides a climate forecasting method and system based on a two-layer ocean model. The method comprises the following steps: acquiring environmental data of a target ocean area; inputting the environmental data into a preset climate forecast model to enable the climate forecast model to perform a plurality of times of cold start operation according to the environmental data and a preset parameter set to obtain a hot start parameter set, updating the current parameter set in each cold start operation process to obtain an updated parameter set, and outputting the updated parameter set to the climate forecast model; if the updated parameter set meets a preset hot start condition, determining that the updated parameter set is the hot start parameter set; performing hot start operation through the climate forecast model according to the hot start parameter set, and outputting predicted time sequence data of the target ocean area in a preset time period; and according to the prediction time sequence data, judging whether the target ocean area has climate abnormality, thereby improving the efficiency of performing climate prediction by using the two-layer ocean model.

The invention discloses a geographic dependency coupling parameter optimization method based on four-dimensional set variation analysis, and belongs to the technical field of ocean data assimilation. Comprising the steps that independent points are selected, a parameter initial disturbance field set is generated, A-4DEnVar data assimilation is carried out, and full-grid parameters are obtained based on independent point parameter value interpolation. According to the method, the mode parameters closely related to the interaction of the coupling components in the coupling mode are optimized based on the A-4DEnVar data assimilation method under the strong coupling data assimilation framework, the influence of strong nonlinearity caused by the coupling mode can be well treated, meanwhile, an adjoint mode is not needed, the problem that the calculation amount of a traditional algorithm is huge is solved, and the method is suitable for large-scale popularization and application. And the problem of simulation precision reduction caused by globally unified coupling parameters is avoided. A corresponding independent point selection scheme does not need to be formulated for a specific problem, and the workload of manual point selection is also reduced. In conclusion, the coupling mode deviation can be efficiently corrected by utilizing the method, technical support is provided in the aspect of improving the accuracy of climate prediction and forecasting, and the method has a relatively good application prospect.

The invention discloses a comprehensive meteorological early warning system based on multi-source data fusion and artificial intelligence, and the system comprises a climate data collection module which is used for obtaining climate data; the climate data analysis module is used for receiving the climate data, calculating the climate data through an artificial intelligence algorithm and combining with a pre-constructed climate model to carry out climate prediction to obtain a prediction result; and the early warning module receives the prediction result and generates early warning information according to the prediction result. The historical climate data, the satellite remote sensing climate data and the real-time weather monitoring data are integrated, the climate data analysis module analyzes and processes the data to obtain the prediction result, and the early warning module generates the early warning information according to the prediction result, so that the prediction accuracy is ensured, the intelligence and the dynamics of the early warning mechanism are realized, and the early warning efficiency is improved. Powerful support can be provided for the development of strategic emerging industries.

The invention discloses a multi-stage coupled climate mode optimization and uncertainty quantification method and system, and relates to the technical field of climate prediction, and the method comprises the steps: obtaining a multi-source meteorological data set, and carrying out the preprocessing of the multi-source meteorological data set; on the basis of the preprocessed multi-source meteorological data set, a multi-dimensional evaluation index system is used for screening a GCM combination with the best adaptability to a research area; based on the screened GCM combination, establishing a segmented statistical relationship between the simulation data and the measured data in the historical period, obtaining a correction factor, and performing deviation correction on the original output of the mode by using the correction factor; and on the basis of a deviation correction result, fitting the GCM data in each scene by adopting a common least square method and a smooth fourth-order polynomial, respectively calculating the variance and proportion of each uncertainty component, and obtaining the total uncertainty through linear superposition. According to the invention, the problems of large atmospheric circulation mode selection deviation, low correction precision and insufficient uncertainty management in the prior art are solved.

The invention belongs to the technical field of artificial intelligence, and discloses an AI digital human-based climate prediction system and method, and the system comprises a meteorological data processing module which obtains domestic and overseas meteorological data through a network, comprises a numerical mode result and observation data, carries out the data cleaning and quality control, and guarantees the accuracy and integrity of the data; the artificial intelligence model training module is used for analyzing and training the meteorological data and establishing a climate prediction model, and the climate prediction model comprises the processes of feature extraction, model training and parameter optimization of the meteorological data; the digital human model rendering module is used for rendering an objective climate prediction result into a digital human image based on a digital human technology, and the digital human image comprises appearance design, speech synthesis and action design processes of a digital human; and the prediction result presentation module is used for presenting the digital human image to the user in various forms, and the user can communicate and communicate with the digital human through an interactive interface, know the climate prediction result and make a corresponding decision.

The invention provides a glacier dynamic early warning method and system, and the method comprises the steps: enabling a core agent to adjust a base sliding coefficient of a target model according to the melt water amount of a target glacier body in a past observation time period through employing the capability of an agent AI for learning a complex mode from data and the preciseness of an ice flow model in dynamics deduction, and achieving the dynamic early warning of the glacier. And the target model carries out deduction prediction based on climate forecast data on the basis of the adjusted base sliding coefficient so as to generate a prediction change result of the target glacier body in a prediction time period, and the accuracy and robustness of the prediction change result are guaranteed by accurately setting the base sliding coefficient under the condition that the two modes are combined. The risk assessment model obtains an instability index of the target glacier body in the prediction time period according to the prediction change result; and the risk assessment model triggers early warning when the instability index exceeds a corresponding dynamic threshold value, so that accurate implementation of early warning is ensured.

The invention relates to the technical field of climate prediction, and discloses a microclimate prediction method and system based on physical information multi-task learning, a terminal and a storage medium, and the method comprises the steps: obtaining a collaborative prediction target of a wind speed task, a temperature task and a humidity task, and constructing a microclimate prediction model based on multi-task learning; training the microclimate prediction model to obtain a trained microclimate prediction model; and acquiring spatial feature information and time sequence weather information of the target block, and inputting the spatial feature information and the time sequence weather information into the trained microclimate prediction model to obtain a microclimate prediction result of the target block. According to the method, a collaborative prediction framework based on a multi-task learning mechanism is provided, a layered and shared multi-task architecture and a physical consistency loss function are constructed, a loss weighted optimization mechanism is adopted, combined prediction of the wind speed, the temperature and the humidity is achieved, the prediction precision and efficiency are improved, meanwhile, the generalization ability of the model is enhanced, and the prediction efficiency of the wind speed, the temperature and the humidity is improved. And the overall prediction effect of the model is obviously improved.

The invention relates to the technical field of climate prediction, in particular to a climate prediction correction method, system and device based on machine learning and a medium, and the method comprises the steps: operating a CMA-CPSv3 climate prediction system, and obtaining an original climate mode prediction data sequence; inputting the original climate mode prediction data sequence into a pre-trained intelligent grid climate prediction model, and outputting a corrected rainfall prediction data sequence; obtaining an actually measured rainfall observation data sequence and an original climate mode prediction data sequence of a corresponding time period after rainfall in the target prediction area; and evaluating the prediction performance of the intelligent grid climate prediction model based on the actually measured rainfall observation data sequence and the corresponding original climate mode prediction data sequence, and updating the intelligent grid climate prediction model according to an evaluation result. According to the method, the gridding data are output through the intelligent grid climate prediction model, the parameters and the closed-loop process are dynamically updated based on the actually measured data, and high-precision, self-adaptive and efficient correction of regional rainfall prediction is realized.

The invention relates to the technical field of climate data processing, and discloses a climate data correction method and system, and the method comprises the steps: obtaining first to-be-corrected data; performing a first screening operation on the first to-be-corrected data to obtain a first screening result, and generating three-channel feature data; inputting the three-channel feature data into a pre-trained first correction network model to obtain three-channel feature data correction output; performing a first division extraction operation on the first to-be-corrected data to obtain a regional feature matrix; and performing first fusion operation on the three-channel feature data correction output and the regional feature matrix to obtain multi-source climate data collaborative correction output. According to the method, the climate data correction precision can be remarkably improved, correction errors are reduced, high-reliability data support is provided for climate prediction, disaster early warning, agricultural production and new energy development, and the development of meteorological services towards the precision and intelligence direction is powerfully promoted.

The invention provides a plateau rainfall prediction method, system and device based on intelligent similarity correction, and relates to the technical field of short-term climate prediction.The method comprises the steps that mode rainfall data of a month to be predicted, multivariable historical factor data corresponding to the mode rainfall data and global historical annual rainfall error data are obtained, determining standard grid point data of the wind field, the height field and the air temperature and effective historical annual rainfall error data; inputting the standard grid point data and the effective historical annual rainfall error data into a pre-trained error prediction model to obtain a prediction error field output by the error prediction model; and based on the prediction error field, correcting the mode precipitation data to obtain the final predicted precipitation. According to the method, multivariable information is integrated, the nonlinear synergistic effect relationship is mined more deeply, the description capability of the rainfall anomaly driving mechanism of the to-be-predicted region is enhanced, and compared with a traditional single-variable or linear scheme, the accuracy of similar selection and rainfall season prediction is effectively improved.

The invention belongs to the technical field of hydrology and water resource and climate prediction crossing, and discloses a medium-and-long-term runoff prediction method fusing a data enhancement technology and a machine learning model, which comprises the following steps: collecting runoff data and climate system index data of a target area, performing factor screening by adopting a replacement accuracy method, the method comprises the following steps: dividing data into a training set and a test set, constructing a time lag factor, carrying out SMOTE data enhancement on the training set, carrying out runoff prediction model construction by using a support vector machine regression model, carrying out parameter optimization by using grid search, calculating an evaluation index to carry out model performance evaluation, and finally predicting and outputting a medium and long-term runoff time sequence according to the model. The method effectively solves the problem of low prediction precision caused by data scarcity and insufficient factor selection in a traditional method. The method overcomes the problems of data scarcity, imbalance and insufficient model generalization ability in medium and long term runoff prediction, and has wide application prospects in the fields of medium and long term runoff prediction and water resource management.

The application belongs to the field of sub-seasonal climate prediction, and provides a sub-seasonal climate prediction method and system based on the combination of dynamic model downscaling and machine learning downscaling, which comprises the following steps: S1, generating initial field and model boundary field information required for dynamic downscaling based on global climate model output data; S2, driving regional climate model to perform dynamic downscaling by using the initial field and model boundary field information; S3, generating input field required for machine learning downscaling based on the output circulation field information of the regional climate model; S4, performing machine learning downscaling correction optimization on the output circulation field of dynamic downscaling based on a convolution model; S5, performing machine learning super-resolution based on the output data of machine learning downscaling correction; and S6, generating sub-seasonal prediction information of double downscaling of dynamic model and machine learning. The application utilizes the complementary advantages of dynamic downscaling and deep learning downscaling, improves the sub-seasonal prediction skill, and copes with new challenges brought by climate change.

The invention discloses a conditional multi-mode gathering method and device for improving ENSO cycle simulation, and relates to the technical field of climate prediction and numerical simulation. The method comprises the following steps: acquiring ENSO simulation results of a plurality of climate modes; calculating a correlation coefficient between the ENSO simulation result and the ENSO observation result of each climate mode; determining a threshold value based on the performance index of each climate mode; dividing climate modes into a positive correlation class, a negative correlation class and a weak correlation class according to the relation between the correlation coefficient and a threshold value; multiplying the simulation result of the negative correlation climate mode by-1 to obtain a correction result of the negative correlation climate mode; and carrying out set average on the correction result of the negative correlation climate mode and the simulation result of the positive correlation climate mode to obtain a final improved ENSO simulation result. According to the method, the consistency of each feature of the ENSO is simulated, the simulation of climate change influencing the ENSO is improved, and the accuracy of ENSO cycle simulation and the reliability of future estimation are improved.

The invention provides a rainfall climate prediction system based on multiple machine learning algorithms, which belongs to the technical field of climate prediction, and comprises a data acquisition unit, a statistical analysis unit, a data decoding module, a key factor screening module, a key factor local cache module and a prediction unit, the output end of the statistical analysis unit and the output end of the data decoding module are connected with the key factor screening module, the key factor screening module is connected with the key factor local cache module, and the key factor local cache module is connected with the prediction unit. According to the method, rainstorm climate prediction analysis is carried out, rainstorm day number prediction based on a numerical mode, Chinese rose scale rainstorm concentration degree climate prediction and season-year scale rainstorm concentration period climate prediction are provided, and reference is provided for rainstorm disaster climate prediction business service.

The invention discloses a climate prediction method and device based on hot start and background constraint and a medium. The method comprises the following steps: training an auto-encoder by using actual climate data in a first historical annual interval, and taking the auto-encoder as an initial climate prediction model decoder; obtaining first climate data of a current year and a second historical year interval predicted by a specified climate prediction model; for each month in the second historical annual interval, generating a sample pair of the month by taking the first climate data of the month and a preset number of months as an input feature and taking the actual climate data of the month as a label; the training sample library is used for training a climate prediction model trained in the last year with the minimum joint loss as the target, and the joint loss comprises a precision constraint representing model prediction performance, a climate background constraint and a prediction score constraint; the trained model is used for predicting the climate of the current year. The continuous evolution and stable prediction of the prediction model are realized, and the convergence speed of the prediction model is improved.

The invention provides an agricultural climate resource evaluation system based on climate mode driving, and the system can generate future climate estimation data through the cooperation of a plurality of modules including data acquisition, preprocessing, statistical downscaling, mode evaluation, multi-mode collection, agricultural climate resource calculation, visual output and the like. According to the system, the prediction precision can be adjusted according to actual observation data, automatic data downloading and space-time matching are achieved through a Python script library, and a multi-level data output module provides provincial and city-county scale evaluation reports and an interactive visual platform. And real-time query and decision support is provided for the user, so that the precision and reliability of climate prediction are remarkably improved.

The present invention relates to the field of climate prediction technology and discloses a microclimate prediction method, system, terminal, and storage medium based on physical information multi-task learning. The method comprises: obtaining collaborative prediction targets for wind speed tasks, temperature tasks, and humidity tasks, and constructing a microclimate prediction model based on multi-task learning; training the microclimate prediction model to obtain a trained microclimate prediction model; obtaining spatial feature information and time-series meteorological information of a target block, and inputting these into the trained microclimate prediction model to obtain a microclimate prediction result for the target block. The present invention proposes a collaborative prediction framework based on a multi-task learning mechanism. By constructing a hierarchical and shared multi-task architecture and a physical consistency loss function, and adopting a loss-weighted optimization mechanism, it achieves joint prediction of wind speed, temperature, and humidity. While improving prediction accuracy and efficiency, it also enhances the generalization ability of the model, significantly improving the overall prediction effect of the model.

The present invention relates to the field of urban waterlogging control, and in particular to a method for predicting water accumulation in a sunken bridge based on downscaling and dynamic division of catchment areas. S10: Predicting rainfall in the sunken bridge area based on large-scale climate forecast factors in the sunken bridge area; S20: Dividing the sunken bridge area into a main catchment area and multiple secondary catchments, and obtaining elevation data for the main catchment area and each secondary catchment area; S30: Determining the risk rating of each secondary catchment area based on rainfall in the sunken bridge area, the elevation of the main catchment area, and the elevation data of each secondary catchment area; S40: Obtaining the amount of water flowing into the main catchment area from each secondary catchment area based on the risk rating of each secondary catchment area; S50: Obtaining the total amount of water accumulated in the main catchment area based on the amount of water flowing into the main catchment area from each secondary catchment area and the amount of water accumulated in the main catchment area itself. The present invention can accurately predict rainfall in the sunken bridge area, and at the same time, dividing the catchment area according to the risk rating can reduce computer computing load and improve prediction accuracy.

The invention relates to the field of climate prediction, in particular to a regional coupling climate extension period prediction method and system. The method comprises the following steps: acquiring ocean water depth basic data, and optimizing the ocean water depth basic data to obtain ocean water depth optimized data; constructing a regional ocean model based on the ocean water depth optimization data; constructing a weather research and forecast model, and constructing a coupler interpolation parameter file based on the weather research and forecast model and the regional ocean model; constructing a coupling mode prediction system of the target region by utilizing the weather research and forecast model and the regional ocean model based on a coupler interpolation parameter file; and obtaining climate extension period prediction data by using the coupling mode prediction system based on a global atmospheric seasonal prediction product and a global ocean prediction product which are obtained in advance. The problem that in the prior art, climate extension period prediction is not accurate enough is solved, and the accuracy of climate extension period prediction is improved by optimizing ocean water depth data and a coupling mode prediction system.

The invention discloses an intelligent stratum exploration and analysis system and method based on artificial intelligence, and relates to the technical field of stratum exploration and analysis, and the method comprises the steps: obtaining a stratum exploration record of a region in a current period, analyzing the deformation condition and the slope safety state of the stratum of the region, and evaluating the soil layer water content condition of the stratum of the region; analyzing a stratum abnormal risk in the region to obtain a target region; analyzing reference values of other areas on the target area in stratum exploration to obtain a reference area; acquiring historical natural disaster records of the reference area, acquiring climate forecast data of the target area in the current period, and evaluating the natural disaster occurrence risk of the target area to obtain a risk target area; the disaster condition of the risk target area is predicted to obtain disaster prediction data, and the disaster prediction data is subjected to information push through the platform, so that the accuracy of early warning and natural disaster risk assessment is improved, and the harm caused by natural disasters is reduced.

The present invention discloses an artificial intelligence-based extended-scale prediction method for ocean climate, which specifically relates to the field of ocean climate prediction, including fusing and standardizing multi-source spatiotemporal data, extracting multi-scale features, generating extreme event samples, and then conducting joint training to generate adaptively optimized prediction results. An artificial intelligence-based extended-scale prediction method for ocean climate effectively reduces the prediction error amplification caused by insufficient cross-scale interaction through a cascaded spatiotemporal attention mechanism; based on a conditional variational autoencoder, it supplements historical sparse samples through physical encoding and residual correction mechanisms, reduces the model's dependence on training set coverage, and improves the generalization ability of extreme event features; by dynamically calculating the feature correlation matrix of atmospheric and ocean modes using a gated recurrent unit, and introducing a conservation law loss function to constrain the feature evolution direction, the correlation and physical consistency of cross-scale features are strengthened.

The invention relates to the technical field of hydro meteorology, in particular to a method and a system for predicting and tracing extreme hydrological events in a reservoir area. The method comprises the following steps of S1, collecting meteorological and hydrological data, a global climate mode data set, geographic space data and stable isotope data of a reservoir area, and unifying a multi-source data scale through a time sequence fusion algorithm; according to the method, isotope data is adopted as an independent evidence constraint model, and compared with a traditional single model method, the method can more comprehensively capture a complex mechanism of occurrence of an extreme event, so that a simulation result is closer to an actual situation, and countermeasures can be taken in the face of an extreme climate event; the climate prediction and the isotope traceability technology are coupled for the first time, how causes of the extreme events change under different future climate scenes can be pre-judged in advance, and the research method and the model framework are not only suitable for specific areas, but also can be widely applied to research of the extreme events of large reservoirs and drainage basins.

The present invention relates to the field of climate forecasting technology, and in particular to a multi-model ensemble sea temperature three-step sub-seasonal climate forecasting method and system. The method comprises the following steps: obtaining sea surface temperature data and sea ice coverage data, and pre-processing the sea surface temperature data and the sea ice coverage data to obtain standard input data; constructing an input field for an atmospheric circulation model based on the standard input data; inputting the input field into the atmospheric circulation model, and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model; and performing dynamic downscaling based on a regional climate model according to the atmospheric circulation information and the surface information to obtain high-precision sub-seasonal climate forecast results. The present invention effectively solves the problem of insufficient accuracy of existing forecasting technologies, improves the accuracy and stability of sub-seasonal climate forecasts, provides new ideas for sub-seasonal climate forecasts, improves sub-seasonal climate forecasting techniques, and enhances the accuracy of forecast products.

The invention relates to the technical field of climate prediction, in particular to a high-resolution refined underlying surface sub-seasonal climate prediction method and a related device, and the method comprises the steps: respectively carrying out the correlation analysis according to monthly average analysis data, first monthly average reanalysis data, a weather reanalysis data set and a weather forecast center reanalysis data set, obtaining a plurality of key regions and a plurality of buffer regions, screening to obtain a target simulation region, a target time step size and a target spatial resolution, and obtaining a first region climate mode; processing according to an output result of the third-generation climate prediction system and the first region climate mode to obtain a target initial boundary field; obtaining a target initial field, an initial configuration file and a target region climate mode according to the first region ground data and an output result of a preset third-generation climate prediction system; and performing simulation analysis processing according to the target instruction data to obtain target prediction data. According to the method, the problem of regional high-precision and high-accuracy sub-seasonal climate prediction is solved.

This invention discloses an intelligent generation method for reservoir flood control scheduling maps based on adaptive control, belonging to the field of reservoir flood control technology. It includes: capturing the interannual fluctuations and trends of future runoff through long-term predicted runoff sequences corrected by VMD-LSTM, providing high-quality input data for multi-objective optimization models; and daily forecasts of 1-7 days provided by short-term flood rolling forecast models, which facilitates real-time integration into the optimization process through dynamic weighting mechanisms, achieving seamless coupling between forecast information and scheduling decisions. The collaborative architecture of long-term and short-term forecasts ensures both the climate adaptability of long-term operating rules and enhances the emergency response capability of real-time scheduling to extreme hydrological events. This invention realizes full-chain adaptive control from climate prediction to scheduling decisions, significantly improving the flood control and comprehensive water use benefits in complex watersheds.

This invention discloses a climate prediction method and apparatus based on observation constraints and a multi-time-dependent sliding window. The method includes: acquiring the first climate data for the current and historical years predicted by a specified climate system model; for each month in the historical year: using the first climate data of that month and a predetermined number of previous months as input features, and the actual climate data of that month as labels, generating sample pairs for that month and adding them to a training sample library; training a pre-constructed climate prediction model using the training sample library with the objective of minimizing the joint loss, the joint loss including the root mean square error, spatial anomaly correlation coefficient, and prediction score characterizing the model's prediction performance; for any month to be predicted in the current year: inputting the first climate data of that month and a predetermined number of previous months into the trained climate prediction model, and outputting the climate prediction result for that month. This invention improves the reliability and fine spatiotemporal resolution of climate prediction.

According to the monthly-scale rainstorm disaster risk dynamic estimation method and system based on the climate prediction mode provided by the invention, rainstorm rainfall threshold value calibration, rainstorm disaster risk assessment, risk grade division and threshold value determination in the climate prediction mode are realized, and the standardization and objectification level of rainstorm disaster risk estimation is improved. According to the monthly-scale rainstorm disaster risk dynamic estimation method and system based on the climate prediction mode, a certain deviation exists between day-by-day rainfall data calculated based on the history of the climate prediction mode and day-by-day rainfall observation data of a national meteorological station (nationality station for short); the observation value and the mode data are adopted to calibrate the rainstorm rainfall threshold value of the climate prediction mode, the rainstorm process is identified and extracted according to the calibrated rainstorm rainfall threshold value, and the rainstorm process identification accuracy is improved.

The invention discloses a multi-stage coupled climate mode optimization and uncertainty quantification method and system, and relates to the technical field of climate prediction, and the method comprises the steps: obtaining a multi-source meteorological data set, and carrying out the preprocessing of the multi-source meteorological data set; on the basis of the preprocessed multi-source meteorological data set, a multi-dimensional evaluation index system is used for screening a GCM combination with the best adaptability to a research area; based on the screened GCM combination, establishing a segmented statistical relationship between the simulation data and the measured data in the historical period, obtaining a correction factor, and performing deviation correction on the original output of the mode by using the correction factor; and on the basis of a deviation correction result, fitting the GCM data in each scene by adopting a common least square method and a smooth fourth-order polynomial, respectively calculating the variance and proportion of each uncertainty component, and obtaining the total uncertainty through linear superposition. According to the invention, the problems of large atmospheric circulation mode selection deviation, low correction precision and insufficient uncertainty management in the prior art are solved.