How to make XGBOOST capture trend in time series forecasting?

I am trying to forecast some sales data with monthly values, I have been trying some classical models as well ML models like XGBOOST.

My data with a feature set looks like this with a length of 110 months and I am trying to forecast for next 12 months,

When it comes to XGBOOST, I've been spending time mostly on hyperparameter optimization with Gridsearch and also state-of-art packages like optuna. My currently best set of parameters looks like this,

parameters = {
            'n_estimators': [700, 1000, 1400],
            'colsample_bytree': [0.7, 0.8],
            'max_depth': [15,20,25],
            'reg_alpha': [1.1, 1.2, 1.3],
            'reg_lambda': [1.1, 1.2, 1.3],
            'subsample': [0.7, 0.8, 0.9],
            'learning_rate': [0.2, 0.3, 0.4],
            'min_child_weight': [1]}
        
        skrg = XGBRegressor(objective = 'reg:linear')
        
        fit_params={early_stopping_rounds:50, 
            eval_metric : rmse, 
            eval_set : [[X_test, y_test]]}
        
        search_sk = GridSearchCV (
        skrg, parameters, cv=TimeSeriesSplit(n_splits=cv).get_n_splits([X_train, y_train]),
                        scoring='neg_mean_squared_error',
                            ) # 5 fold cross validation
        search_sk.fit(X_train, y_train, **fit_params)

with results like,

I couldn't figure out how to model for the upwards trend in the data. Does it come under the optimization or do I have to do something with my data like transformations or adding extra features. Any advices? Thanks!