## 1 Introduction
[41] distinguished between logistic or Poisson regression if the objective was fire probability or number of fires, respectively. [PERSON] et al. [42] also separately used logistic regression in Portugal in fires of more than 5 km\({}^{2}\), concluding that these wildfires are more likely to occur in low populated and far-away places. In the last decades, there have also been many studies of wildfires in Spain. For instance, [PERSON] et al. [43] used the logistic regression analysis to quantify changes in future fire occurrence due to future land use/land cover variations, [PERSON] et al. [44] and [PERSON] et al. [29] explored the prediction of summer wildfires by developing a multiple linear regression model, [PERSON] et al. [45] employed the maximum entropy algorithm to analyze the intra-annual dimension of fire occurrence, [PERSON] et al. [46] explored the use of binary structured additive regression for prediction of wildfires, and [PERSON] et al. [47] assessed the spatial-temporal changes in the contribution of wildfire drivers using geographically weighted logistic regression models. Concerning artificial intelligence techniques, [PERSON] et al. [48] obtained better results using an artificial neural network (ANN) than logistic regression. Indeed, in recent years computational intelligence models have often been used to perform forest fire prediction [10]. [PERSON] [49] created software using fuzzy logic for the prediction of fire risk in Greece. [PERSON] and [PERSON] [50; 51] used data-mining techniques and an ANN to estimate the burned area in Canada. [PERSON] and [PERSON] [52] estimated forest fire loss in Portugal using multivariate regression, decision trees, random forest, an ANN, and a support vector machine (SVM), obtaining the best results with SVM. [PERSON] et al. [28] used genetic programming in Portugal, too. [PERSON] and [PERSON] [53] also applied an ANN and an SVM in Lebanon using only humidity and precipitation as inputs. [PERSON] and [PERSON] [10] investigated the size of a fire after its first breakout through an ANN, radial basis function networks, an SVM and fuzzy logic. The ANN model with humidity and wind speed as inputs, and three clusters (small, medium, and big fires) proved to be the best model. [PERSON] et al. [54] compared an ANNmodel and logistic regression to assess forest fire danger. Their results showed good achievements by the ANN technique. Similar results were obtained by [PERSON] et al. (2015) and [PERSON] et al. (2016), who employed ANNs to identify high forest fire risks in Iran and Brazil, respectively. [PERSON] et al. (2017) employed an ANN in a Mediterranean forest ecosystem in Turkey with a coefficient of accuracy of 0.83. This paper investigated the performance of an ANN in burned area size prediction in a semi-arid region in the south-east of Spain and to assess future evolution of big fires in the area concerned under two climate change scenarios. The contents of the paper are structured as follows: data, study area and methodology are shown in Section 2. Section 3 presents results, which are discussed in Section 4. Finally, Section 5 highlights the main conclusions. This work complements a previous study carried out by [PERSON] et al. (2018) where fire weather indices were assessed in the study area.