Methods and systems for climate forecasting using artificial neural networks

A system for generating a neural network (NN)-based climate forecast model, comprising:

• at least one processor;
• and
• a non-transitory physical storage medium for storing program code and accessible by the processor, the program code when executed by the processor causes the processor to:
• generate a multi-model ensemble of global climate simulation data by combining simulation data from at least two of a plurality of global climate simulation models (GCMs);
• pre-process the multi-model ensemble of global climate simulation data, wherein the pre-processing comprises at least one pre-processing action selected from the group consisting of spatial re-gridding, temporal homogenization, and data augmentation;
• train the NN-based climate forecast model on the pre-processed multi-model ensemble of global climate simulation data;
• and
• validate the NN-based climate forecast model using a first set of observational historical climate data.

A non-transitory physical storage medium for generating a neural network (NN)-based climate forecasting model, the storage medium comprising program code stored thereon, that when executed by a processor causes the processor to:

• generate a multi-model ensemble of global climate simulation data by combining simulation data from at least two of a plurality of global climate simulation models (GCMs);
• pre-process the multi-model ensemble of global climate simulation data, wherein the pre-processing comprises at least one pre-processing action selected from the group consisting of spatial re-gridding, temporal homogenization, and data augmentation;
• train the NN-based climate forecasting model on the pre-processed multi-model ensemble of global climate simulation data;
• and
• validate the NN-based climate forecasting model using a first set of observational historical climate data.