The very first DLWP models were developed in the 1990s and followed a “feed-forward architecture” , a unidirectional, non-recurrent structure in which the input is transmitted through the network sequentially. Feed-forward neural networks (FNNs) are limited in treating spatial data, due to their inability to leverage spatial patterns and their large computational burden, which makes them unsuitable for large datasets. For these reasons, FNNs were soon replaced by convolutional neural networks (CNNs; ), which can learn spatial patterns and display better scalability. Early meteorological applications of CNNs had either a very local character or were aimed at producing nowcasts with lead times from a few minutes to a few hours .

A further step in the direction of today's medium-range DLWP models was taken with the adoption of recurrent neural network (RNN) architectures () and, subsequently, long short-term memory (LSTM) models (). These follow the dynamical nature of time-series data by making current observations of the variable of interest depend on previous iterations of that same variable. Thus, they provide an effective framework for accounting for time dependencies in the data and produce predictions on multiple timescales. However, due to their sequential, recursive nature, RNNs and LSTMs are hard to parallelise, preventing an effective exploitation of modern high-resolution climate reanalysis datasets, such as ERA5 .

FNNs, CNNs, and RNNs/LSTMs are the cornerstones of deep learning and were also the dominant supervised learning architectures within DLWP at the time that the review by was written. Since then, a number of new architectures have been developed that address the limitations of the classical models via a number of creative innovations, often combining different elements of pre-existing architectures. Here, we focus primarily on those deep learning applications that are most relevant to medium-range, large-scale forecasting of weather extremes. Nonetheless, we acknowledge that data-driven nowcasting and subseasonal forecasting are both thriving fields of research with many potential applications, including for extreme events e.g..

A common element of current global medium-range DLWP models is the use of a large number of input variables (“features”) at a high temporal and spatial resolution. This is in contrast to older models that, to a large extent, relied on a theoretical understanding of atmospheric dynamics and feature selection for the choice of a few atmospheric variables and pressure levels e.g.. For example, include 6 input variables on 37 pressure levels, 5 inputs at single levels, and several constant masks. Similarly, make use of 4 atmospheric variables at 13 pressure levels, 4 surface variables, and 3 constant masks.

The fact that the latest DLWP models make use of a larger number of features than prior models may be partly ascribed to (1) computational improvements and (2) deep learning architectural developments. A key factor in this respect is the encoder–decoder architecture . Encoders and decoders can be seen as two separate neural networks connected to each other through a latent encoding vector. Here, “latent” refers to quantities inferred indirectly from the input data. The aim of the first network is to identify and compress (“encode”) into the encoding vector the most important features contained in the input data. The aim of the second network is to upscale (“decode”) the information encoded in the encoding vector until it reaches the dimensionality of the desired output. The target output can then either be the same as the input, perhaps with some small variation (self-supervised problems, e.g. variational autoencoders), or different from the input in terms of timescale, spatial resolution, or even actual features.

In DLWP, the target output is usually different from the input, and encoders are mostly used to reduce the dimensionality of the input and identify the key latent features. This allows models to use very large input layers, namely, many different atmospheric variables at several pressure levels. Encoder–decoder architectures are used to this effect within cutting-edge global DLWP applications, such as , , , and .

The encoder–decoder structure is also at the core of transformers , a recent architectural innovation allowing for efficient parallelisation of sequential data. Transformers use a so-called attention mechanism , i.e. they compute a score for each element in the input sequence which determines its relevance for the associated decoding step. This removes the need for sequential data intake, thereby enabling an effective utilisation of modern GPUs and TPUs for time-series data. This represents a major improvement over classic RNNs, which instead rely on serial arrangement to learn key features and are, therefore, not easily parallelisable.

Recently, the use of transformers has been extended to computer vision tasks as an alternative, or complement, to CNNs. propose the use of vision transformers, which adapt transformers to visual tasks by introducing an innovative preprocessing step: images are first divided into patches of fixed N×N size and then run through a flattening layer, so that each patch can be treated as a separate token. Next, transformers are applied just as in sequential tasks. This approach has been applied to weather forecasting, for instance, by and , who use flattened patches of 4×4 pixels to apply transformers to gridded meteorological data.

A distinct approach featured by several global DLWP models is the use of graph neural networks (GNNs; ). Classic CNNs implicitly assume regular grids, in which the distance between points and the importance of each point is fixed . This assumption is problematic in the case of global forecast models, as climate variables are often provided on regular latitude–longitude or reduced-Gaussian grids. Given that the Earth is quasi-spherical, the distance between degrees of latitude is greater at the Equator than at the poles, and even the length of degrees of longitude varies slightly with latitude. GNNs, unlike CNNs, allow for complex, quasi-spherical shapes. A way to understand this is by drawing a parallel with Cartesian and spherical coordinates. CNNs, similar to Cartesian coordinates, assume a “flat” grid and, in the best case, may introduce a weighting scheme not unlike the one of the β-plane, whereas GNNs can model the relation between the nodes as a complex polygon resembling a sphere.

The bearing of the architectural change from CNNs to GNNs on forecast performance is still the object of debate, and it is likely that it plays a larger role for global rather than local-to-regional applications, given the greater variation in the size of the grid cells in the former case. However, several recent deep learning weather forecasting models have introduced the use of GNNs to good effect. For instance, and, more prominently, have made use of GNNs to obtain accurate medium-range forecasts of several key atmospheric variables, managing to outperform the most accurate ECMWF deterministic forecasts available at the time of their publication.

Other current approaches look at ways of accounting for Earth's quasi-spherical nature within a CNN framework, without resorting to GNNs. Examples of this include spherical convolutions and spherical cross-correlations . Recent work by showcases the advantages of spherical and hemispheric convolutions over classic CNNs. The authors compare models based on different architectures using the WeatherBench dataset and show that models incorporating spherical or hemispheric convolutions produce more accurate medium-range forecasts of the 500 hPa geopotential height (Z500) and 850 hPa temperature (t850) than models featuring classic CNN architectures. However, feature-rich and high-resolution applications of this kind are still under development.

Finally, we outline how recent medium-range DLWP models treat temporal information. Instead of trying to incorporate the time aspect directly into the model in the form of extra features or channels, they account for the sequential nature of data through a dynamic approach, by using the predictions generated by a given model time step as the input for the next model time step. In other words, as clearly stated by , they approximate the forecast at time t, Yt=f(Yt-1+Yt-2+…+Y1) through an autoregressive approach of order 1 (AR 1), namely, by sequentially using the forecasts at the previous time steps: Yt=f(Yt-1),Yt-1=f(Yt-2),…,Y2=f(Y1). A similar approach is adopted by , with the main difference being that the data generated by the previous two forecasts are used as input for the latest forecast (AR 2).

If the focus is on a specific lead time, it is, however, not clear whether this iterative approach always outperforms training a model to make a single prediction at the chosen lead time . In this regard, show that producing a cascade of models fine-tuned on different timescales can lead to improvements in performance compared with a single model optimised on the whole forecasting window.

An overview of the DLWP model developments over time is provided in Figs.  and . Figure  summarises the evolution of model architectures described in this section, while Fig.  outlines the continuous improvements in the spatial and temporal domains handled by DLWP models. The current leading global DLWP models are systematically presented in Table , where we provide information on the inputs, outputs, main architectural innovations, and performance for extreme weather forecasts of each model.

A common limitation of most large-scale DLWP applications introduced so far is that they are not targeted in any specific way at extreme weather events; rather, their focus lies on maximising the average skill of the forecasts. Typically, machine learning models struggle to make accurate predictions of extreme values, partly due to (1) the inherently limited training samples for extreme values and (2) the intrinsic inferential challenges related to extrapolation. Given the key role of accurate prediction and of early warnings for extreme weather in disaster prevention and risk mitigation , it would be desirable for current DLWP applications to dedicate greater attention to forecast skill for extreme weather .

As highlighted in Table , this problem is further exacerbated by the fact that many global DLWP papers provide no or very limited diagnostics on the performance of their models for extreme weather scenarios e.g., making it hard to assess their performance in those situations. Even those that do provide extreme weather diagnostics mostly focus on selected variables and case studies, supplying no systematic overview of how the models perform in the prediction of high-impact surface extremes such as total precipitation or peak wind gusts. Indeed, some state-of-the-art DLWP models, such as , do not even produce forecasts for those variables.

suggests some simple measures that authors could adopt to help readers evaluate whether or not a machine learning model can provide robust forecasts of extreme events: for instance, that all papers should include scatterplots and quantile–quantile plots of forecasted vs. observed values and that performance metrics computed only on extreme values should complement classic metrics of average skill. A positive note since the release of is that several research groups have chosen to make the code of their global models publicly available, making it possible for third-party actors with enough computational resources to implement and further test their models. For instance, ECMWF has recently launched an experimental programme running daily 10 d forecasts with 6-hourly time steps of the models introduced by , , and , whose forecasts are available to the general public . Similarly, WeatherBench 2 provides additional scorecards and out-of-sample predictions for several models included in Table .

Some key “inductive biases”, i.e. implicit assumptions of the employed estimation techniques , may also hamper the performance of current DLWP applications for extreme weather forecasting. Most global DLWP models choose to minimise the overall mean-squared error (L2) of the forecast, averaging over all grid points and time steps of interest . The minimisation thus uses the conditional mean of the dependent variable through space and time given the predictors, optimising forecasts for mean rather than extreme values. Furthermore, the use of L2 (and also L1, the mean absolute error, used, for instance, by , and ) implicitly assumes that, for any given variable, the distribution of the forecast error is symmetric, i.e. that it is possible to obtain both positive and negative errors of the same magnitude, and that deviations from the modelled value in the two directions are equally important. This is seldom the case in weather forecasting. Many weather variables display a high degree of autocorrelation and follow highly asymmetric truncated distributions (e.g. peak wind speed or precipitation), which in combination tend to produce non-asymmetric error distributions . Moreover, deviations of a variable from its mean in one of the two directions can have larger impacts on human societies than deviations in the other direction (e.g. one would expect that severely underestimating the amount of rain in a flash-flood event would be more harmful than incorrectly predicting rain on a dry day).

While the suggestions in , if implemented, would go a long way in ensuring greater transparency and credibility for DLWP forecasts of extreme weather, the challenges related to the extrapolation issue and inductive biases still remain. The limited diagnostics provided by and suggest that their models perform reasonably well on extremes but also that they consistently tend to underestimate their magnitude. Similarly, find that Pangu-Weather can provide high-quality binary forecasts of moderately extreme temperatures but that it also tends to oversmooth the prediction and underestimate the magnitude of the largest cold and hot extremes.

Thus, we argue for the need for DLWP models explicitly built to forecast extremes. These should make use of targeted loss functions and produce robust predictions of all relevant variables at or beyond the limits of their training range. In the next section, we propose a schematic framework on which to build such models. The aim here is to provide a general foundation for such approaches, rather than discussing architectural details. Indeed, several of the architectures adopted by the models in Table  and Sect. 2.1 are, in principle, equally suitable for predictions of extreme weather and average weather. The limiting factors are most likely the choice of the optimisation problem and the lack of a specific treatment of the extremes, rather than the architectures themselves.

A simple way of shifting the focus from the average skill of a deep learning model to its performance in the tails of the distribution is by changing its loss function. Common loss functions, such as the mean absolute error (L1) and the mean-squared error (L2), are minimised by taking the conditional median and mean of the dependent variable, respectively. An alternative loss function is given by the pinball loss, defined as follows : 1Lpinball=1N∑i=1Nmax⁡(τ⋅(yi-y^i),(1-τ)⋅(y^i-yi)), where τ is the target quantile, N is the number of training observations, i represents a specific observation, yi is the actual value of the target variable for that observation, and y^i is the forecast generated by the model. This loss function punishes predictions that are further away from the quantile of interest and is minimised by the conditional quantile of the dependent variable.

As the median is the 50th quantile, the pinball loss is equivalent to L1 when choosing τ=0.5.

Within a deep learning setting, models minimising the pinball loss often go under the name of deep quantile regression or quantile regression neural networks . A limitation of deep quantile regression is that enough observations below and above the quantile of interest need to be available for the model to work properly. This can sometimes be an issue within a DLWP framework, given that the main interest can lie in very extreme quantiles, i.e. seldom-observed extreme events with long return periods.

A solution to this problem has recently been proposed by , who, building upon earlier work by , suggest using a two-step peak-over-threshold approach. First, a quantile-regression-based estimator, such as linear or deep quantile regression, is used to estimate a conditional threshold of interest. Then, the properties of the distribution of the exceedances are modelled with the help of extreme value theory (EVT). assume that, in accordance with and , independent exceedances approximately follow a generalised Pareto distribution, with parameters depending on the value of the regressors. These parameters can then be estimated with the help of a neural network, and the resulting empirical distribution can be used to derive the properties of the distribution of any extreme event of interest, as is commonly done in EVT.

However, even the combination of quantile and EVT-based approaches suffers from a key limitation: it does not provide a deterministic forecast for a given time and place but only return periods or values and a risk ratio of the probability of an event taking place compared to the climatology. In other words, it answers questions such as “Is extreme event `X' more likely to occur than usual on day `Y'?” or “How often does an event of a given severity occur given an initial set of atmospheric conditions?”. It does not answer the question typically associated with deterministic weather forecasts, namely, “Is extreme event `X' going to take place on day `Y' at location `Z'?”.

A possible alternative for cases in which we are interested in answering the latter question, i.e. we want a deterministic forecast of a given extreme event at a specific time and place, is to use a binary classification model. This can, for instance, minimise a binary cross-entropy loss, defined as follows: 2Lbincross=1N∑i=1Nyi⋅log⁡(yi)+(1-yi)⋅log⁡(1-yi). By defining the extreme event on the basis of a threshold or a given quantile of the climatology and minimising Eq. (), we can train a model to estimate the probability of an event of a given magnitude taking place at a specific time and location. The forecasted probability for a specific time and place can then easily be converted into a deterministic forecast by choosing a cutoff probability (e.g. 50 %), where events above that probability are expected to take place and events under that probability are not.

Whenever a heavy class imbalance is present, i.e. the interest lies in very extreme quantiles, training a classification neural network may be challenging, as the model may be prone to reverting to the trivial solution of never predicting an extreme event. In those cases, class weights may help: weights are introduced in Eq. () in order to give greater importance to the loss generated by training samples from the minority class, namely, the extremes. In other words, the model is trained to minimise a weighted cross-entropy loss (Eq. ), defined as follows: 3Lweightedbincross=1N∑i=1N-w1yi⋅log⁡(yi)+w0(1-yi)⋅log⁡(1-yi), where w1 is the weight assigned to observations in the minority class and w0 is the weight assigned to observations in the majority class.

Even after introducing class weights, this approach, like quantile regression, needs enough observations in each of the two classes for the model to work properly. Thus, it is not suitable in isolation for extremes with a very long return period, appearing no or very few times in the training sample. However, as in the previous case, we can build a two-step peak-over-threshold model that addresses this problem. First, we decide through a classification model whether or not an event above a certain not-too-extreme threshold is going to take place. Then, we model the tails of the distribution with the help of the Pickands–Balkema–De Haan theorem , which allows one to make inferences on very extreme cases potentially beyond the model's training range.

The different steps introduced above can be combined in order to obtain forecasts providing a rich set of information. For instance, one may jointly implement a classification-based and a quantile-based deep learning model to obtain time- and location-specific forecasts of an extreme as well as information on its return period. Figure  summarises the approaches described in this section in a simple framework that can be used to tailor deep learning models to extreme weather forecasting.